# Evolutionary algorithm

Evolutionary algorithm
You are encouraged to solve this task according to the task description, using any language you may know.

Starting with:

• The `target` string: `"METHINKS IT IS LIKE A WEASEL"`.
• An array of random characters chosen from the set of upper-case letters together with the space, and of the same length as the target string. (Call it the `parent`).
• A `fitness` function that computes the ‘closeness’ of its argument to the target string.
• A `mutate` function that given a string and a mutation rate returns a copy of the string, with some characters probably mutated.
• While the `parent` is not yet the `target`:
• copy the `parent` C times, each time allowing some random probability that another character might be substituted using `mutate`.
• Assess the `fitness` of the parent and all the copies to the `target` and make the most fit string the new `parent`, discarding the others.
• repeat until the parent converges, (hopefully), to the target.

Note: to aid comparison, try and ensure the variables and functions mentioned in the task description appear in solutions

A cursory examination of a few of the solutions reveals that the instructions have not been followed rigorously in some solutions. Specifically,

• While the `parent` is not yet the `target`:
• copy the `parent` C times, each time allowing some random probability that another character might be substituted using `mutate`.

Note that some of the the solutions given retain characters in the mutated string that are correct in the target string. However, the instruction above does not state to retain any of the characters while performing the mutation. Although some may believe to do so is implied from the use of "converges"

```(:* repeat until the parent converges, (hopefully), to the target.
```

Strictly speaking, the new parent should be selected from the new pool of mutations, and then the new parent used to generate the next set of mutations with parent characters getting retained only by not being mutated. It then becomes possible that the new set of mutations has no member that is fitter than the parent!

As illustration of this error, the code for 8th has the following remark.

```Create a new string based on the TOS, changing randomly any characters which
```

NOTE: this has been changed, the 8th version is completely random now

Clearly, this algo will be applying the mutation function only to the parent characters that don't match to the target characters!

To ensure that the new parent is never less fit than the prior parent, both the parent and all of the latest mutations are subjected to the fitness test to select the next parent.

## 8th

` \ RosettaCode challenge http://rosettacode.org/wiki/Evolutionary_algorithm\ Responding to the criticism that the implementation was too directed, this\ version does a completely random selection of chars to mutate var gen\ Convert a string of valid chars into an array of char-strings:"ABCDEFGHIJKLMNOPQRSTUVWXYZ " null s:/ var, valid-chars \ How many mutations each generation will handle; the larger, the slower each\ generation but the fewer generations required:300 var, #mutations23 var, mutability : get-random-char  valid-chars @  27 rand-pcg n:abs swap n:mod  a:@ nip ; : mutate-string \ s -- s'  (     rand-pcg mutability @ n:mod not if     drop get-random-char    then  ) s:map ; : mutate \ s n -- a   \ iterate 'n' times over the initial string, mutating it each time  \ save the original string, as the best of the previous generation:  >r [] over a:push swap  (  tuck mutate-string  a:push swap  ) r> times drop ; \ compute Hamming distance of two strings:: hamming \ s1 s2 -- n  0 >r  s:len n:1-  (   2 pick over s:@ nip   2 pick rot s:@ nip   n:- n:abs r> n:+ >r  ) 0 rot loop  2drop r> ; var best: fitness-check \ s a -- s t  10000 >r  -1 best !  (   \ ix s ix s'    2 pick hamming    [email protected]    over n:> if      rdrop >r      best !   else      2drop   then  )  a:each  rdrop best @  a:@ nip  ;  : add-random-char \ s -- s'  get-random-char s:+ ; \ take the target and make a random string of the same length: initial-string \ s -- s  s:len "" swap    ' add-random-char  swap times ; : done? \ s1 s2 -- s1 s2 | bye  2dup s:= if    "Done in " . gen @ . " generations" . cr ;;;  then ; : setup-random  rand rand rand-pcg-seed ; : evolve   1 gen n:+!  \ create an array of #mutations strings mutated from the random string, drop the random  #mutations @ mutate    \ iterate over the array and pick the closest fit:  fitness-check   \ show this generation's best match:  dup . cr   \ check for end condition and continue if not done:  done? evolve ; "METHINKS IT IS LIKE A WEASEL"  setup-random initial-string evolve bye`

The output:

```PIQSLOGHISTIPSDLZFGRDBYUCADA
PIQSNOGH SQIPSDLZFG DBYUEDDA
...
METHINKS IT IS LIKD A WEASEL
METHINKS IT IS LIKD A WEASEL
METHINKS IT IS LIKE A WEASEL
Done in 43 generations
```

Very simple fitness determination. For testing purposes you can add a static seed value to the RNG initializations (sample output uses '12345' for both).

`with Ada.Text_IO;with Ada.Numerics.Discrete_Random;with Ada.Numerics.Float_Random;with Ada.Strings.Fixed;with Ada.Strings.Maps; procedure Evolution is    -- only upper case characters allowed, and space, which uses '@' in   -- internal representation (allowing subtype of Character).   subtype DNA_Char is Character range '@' .. 'Z';    -- DNA string is as long as target string.   subtype DNA_String is String (1 .. 28);    -- target string translated to DNA_Char string   Target : constant DNA_String := "[email protected]@[email protected]@[email protected]";    -- calculate the 'closeness' to the target DNA.   -- it returns a number >= 0 that describes how many chars are correct.   -- can be improved much to make evolution better, but keep simple for   -- this example.   function Fitness (DNA : DNA_String) return Natural is      Result : Natural := 0;   begin      for Position in DNA'Range loop         if DNA (Position) = Target (Position) then            Result := Result + 1;         end if;      end loop;      return Result;   end Fitness;    -- output the DNA using the mapping   procedure Output_DNA (DNA : DNA_String; Prefix : String := "") is      use Ada.Strings.Maps;      Output_Map : Character_Mapping;   begin      Output_Map := To_Mapping        (From => To_Sequence (To_Set (('@'))),         To   => To_Sequence (To_Set ((' '))));      Ada.Text_IO.Put (Prefix);      Ada.Text_IO.Put (Ada.Strings.Fixed.Translate (DNA, Output_Map));      Ada.Text_IO.Put_Line (", fitness: " & Integer'Image (Fitness (DNA)));   end Output_DNA;    -- DNA_Char is a discrete type, use Ada RNG   package Random_Char is new Ada.Numerics.Discrete_Random (DNA_Char);   DNA_Generator : Random_Char.Generator;    -- need generator for floating type, too   Float_Generator : Ada.Numerics.Float_Random.Generator;    -- returns a mutated copy of the parent, applying the given mutation rate   function Mutate (Parent        : DNA_String;                    Mutation_Rate : Float)                    return          DNA_String   is      Result : DNA_String := Parent;   begin      for Position in Result'Range loop         if Ada.Numerics.Float_Random.Random (Float_Generator) <= Mutation_Rate         then            Result (Position) := Random_Char.Random (DNA_Generator);         end if;      end loop;      return Result;   end Mutate;    -- genetic algorithm to evolve the string   -- could be made a function returning the final string   procedure Evolve (Child_Count   : Positive := 100;                     Mutation_Rate : Float    := 0.2)   is      type Child_Array is array (1 .. Child_Count) of DNA_String;       -- determine the fittest of the candidates      function Fittest (Candidates : Child_Array) return DNA_String is         The_Fittest : DNA_String := Candidates (1);      begin         for Candidate in Candidates'Range loop            if Fitness (Candidates (Candidate)) > Fitness (The_Fittest)            then               The_Fittest := Candidates (Candidate);            end if;         end loop;         return The_Fittest;      end Fittest;       Parent, Next_Parent : DNA_String;      Children            : Child_Array;      Loop_Counter        : Positive := 1;   begin      -- initialize Parent      for Position in Parent'Range loop         Parent (Position) := Random_Char.Random (DNA_Generator);      end loop;      Output_DNA (Parent, "First: ");      while Parent /= Target loop         -- mutation loop         for Child in Children'Range loop            Children (Child) := Mutate (Parent, Mutation_Rate);         end loop;         Next_Parent := Fittest (Children);         -- don't allow weaker children as the parent         if Fitness (Next_Parent) > Fitness (Parent) then            Parent := Next_Parent;         end if;         -- output every 20th generation         if Loop_Counter mod 20 = 0 then            Output_DNA (Parent, Integer'Image (Loop_Counter) & ": ");         end if;         Loop_Counter := Loop_Counter + 1;      end loop;      Output_DNA (Parent, "Final (" & Integer'Image (Loop_Counter) & "): ");   end Evolve; begin   -- initialize the random number generators   Random_Char.Reset (DNA_Generator);   Ada.Numerics.Float_Random.Reset (Float_Generator);   -- evolve!   Evolve;end Evolution;`

sample output:

```First: FCLYNZAOQ KBSZHJAKAWOSZKBOBT, fitness:  1
20: MKTHCPKS IT MSBBIKEVB SPASEH, fitness:  17
40: METHIDKS IT NS BIKE B OQASET, fitness:  21
60: METHIDKS IT NS BIKE B OQASET, fitness:  21
80: METHIDKS IT NS BIKE B OQASET, fitness:  21
100: METHIDKS IT VS BIKE B WQASEP, fitness:  22
120: METHIDKS IT VS BIKE B WQASEP, fitness:  22
140: METHIDKS ITBVS LIKE B WEASEP, fitness:  23
160: METHIDKS ITBVS LIKE B WEASEP, fitness:  23
180: METHIDKS ITBVS LIKE B WEASEP, fitness:  23
200: METHIDKS ITBIS LIKE B WEASEP, fitness:  24
220: METHITKS ITBIS LIKE B WEASEL, fitness:  25
240: METHITKS ITBIS LIKE B WEASEL, fitness:  25
260: METHITKS ITBIS LIKE B WEASEL, fitness:  25
280: METHITKS ITBIS LIKE B WEASEL, fitness:  25
300: METHITKS ITBIS LIKE B WEASEL, fitness:  25
320: METHITKS ITBIS LIKE B WEASEL, fitness:  25
340: METHITKS ITBIS LIKE B WEASEL, fitness:  25
360: METHITKS ITBIS LIKE B WEASEL, fitness:  25
380: METHINKS ITBIS LIKE A WEASEL, fitness:  27
Final ( 384): METHINKS IT IS LIKE A WEASEL, fitness:  28```

## Aime

Translation of: C
`integerfitness(data t, data b){    integer c, f, i;     f = 0;     for (i, c in b) {        f += sign(t[i] ^ c);    }     f;} voidmutate(data e, data b, data u){    integer c;     for (, c in b) {        e.append(drand(15) ? c : u[drand(26)]);    }} integermain(void){    data b, t, u;    integer f, i;     t = "METHINK IT IS LIKE A WEASEL";    u = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ";     i = ~t;    while (i) {        i -= 1;        b.append(u[drand(26)]);    }     f = fitness(t, b);    while (f) {        data n;        integer a;         o_form("/lw4/~\n", f, b);         n = b;         i = 32;        while (i) {            data c;             i -= 1;            mutate(c, b, u);            a = fitness(t, c);            if (a < f) {                f = a;                n = c;            }        }         b = n;    }     o_form("/lw4/~\n", f, b);     return 0;}`
Output:
```23  EAAXIZJROVOHSKREBNSAFHEKF B
22  EAUHIZJREVOHSKREBNSAFHEKF B
21  IAUHIZJREVOHSKREBESAFHEKF B
20  IKUHIZJRETOTSKREBESAFHEKFWB
20  IKUHIZJRETOTSKREBESAFHEKFWB
19  IKUHIZJRET USKREBESAFHEKFWA
19  IKUHIZJRET USKREBESAFHEKFWA
19  IKUHIZJRET USKREBESAFHEKFWA
18  IKUHIZJRET US REBESAFHEKFWA
18  IKUHIZJRET US REBESAFHEKFWA
17  IKMHIZJKET US REBESA HEKFWA
16  IKMHIZJKET US LEBEJA HEKJWA
16  IKMHIZJKET US LEBEJA HEKJWA
16  IKMHIZJKET US LEBEJA HEKJWA
16  IKMHIZJKET US LEBEJA HEKJWA
15  MKKHIZJ ET US LEBEJF HEKJWA
14  MEEHIZJ ET US LEBEJF HEKJWA
14  MEEHIZJ ET US LEBEJF HEKJWA
13  MEEHIZJ ET US LKBE F OEKJWA
12  MEEHIZJ ET US LKKE F OEKJWA
12  MEEHIZJ ET US LKKE F OEKJWA
11  MEEHIZJ ET US LIKE F OEKJWA
11  MEEHIZJ ET US LIKE F OEKJWA
10  MEEHIZJ IT US LIKE F OEKJWA
10  MEEHIZJ IT US LIKE F OEKJWA
...
1   METHINK IT IS LIKE F WEASEL
1   METHINK IT IS LIKE F WEASEL
0   METHINK IT IS LIKE A WEASEL```

## ALGOL 68

Translation of: C
Note: This specimen retains the original C coding style.
Works with: ALGOL 68 version Revision 1 - no extensions to language used.
Works with: ALGOL 68G version Any - tested with release 1.18.0-9h.tiny.
`STRING target := "METHINKS IT IS LIKE A WEASEL"; PROC fitness = (STRING tstrg)REAL:(   INT sum := 0;   FOR i FROM LWB tstrg TO UPB tstrg DO      sum +:= ABS(ABS target[i] - ABS tstrg[i])   OD;   # fitness := # 100.0*exp(-sum/10.0)); PROC rand char = CHAR:(   #STATIC# []CHAR ucchars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ";   # rand char := # ucchars[ENTIER (random*UPB ucchars)+1]); PROC mutate = (REF STRING kid, parent, REAL mutate rate)VOID:(   FOR i FROM LWB parent TO UPB parent DO      kid[i] := IF random < mutate rate THEN rand char ELSE parent[i] FI   OD); PROC kewe = ( STRING parent, INT iters, REAL fits, REAL mrate)VOID:(   printf((\$"#"4d" fitness: "g(-6,2)"% "g(-6,4)" '"g"'"l\$, iters, fits, mrate, parent))); PROC evolve = VOID:(   FLEX[UPB target]CHAR parent;   REAL fits;   [100]FLEX[UPB target]CHAR kid;   INT iters := 0;   kid[LWB kid] := LOC[UPB target]CHAR;   REAL mutate rate;    #  initialize  #      FOR i FROM LWB parent TO UPB parent DO      parent[i] := rand char   OD;    fits := fitness(parent);   WHILE fits < 100.0 DO      INT j;      REAL kf;      mutate rate := 1.0  - exp(- (100.0 - fits)/400.0);      FOR j FROM LWB kid TO UPB kid DO         mutate(kid[j], parent, mutate rate)      OD;      FOR j FROM LWB kid TO UPB kid DO         kf := fitness(kid[j]);         IF fits < kf THEN            fits := kf;            parent := kid[j]         FI      OD;      IF iters MOD 100 = 0 THEN         kewe( parent, iters, fits, mutate rate )      FI;      iters+:=1   OD;   kewe( parent, iters, fits, mutate rate )); main:(   evolve)`

Sample output:

```#0000 fitness:   0.00% 0.2212 'JUQBKWCHNPJ LO LFDKHDJJNQIFQ'
#0100 fitness:   5.50% 0.2104 'NGVGIOJV IT JS MGLD C VEAWCI'
#0200 fitness:  22.31% 0.1765 'MGTGIOJS IU JS MGKD C VEAREL'
#0300 fitness:  60.65% 0.0937 'METHIOKS IU IS LIKE B VFASEL'
#0354 fitness: 100.00% 0.0235 'METHINKS IT IS LIKE A WEASEL'
```

## AutoHotkey

`output := ""target := "METHINKS IT IS LIKE A WEASEL"targetLen := StrLen(target)Loop, 26	possibilities_%A_Index% := Chr(A_Index+64) ; A-Zpossibilities_27  := " "C := 100 parent := ""Loop, %targetLen%{	Random, randomNum, 1, 27  parent .= possibilities_%randomNum%} Loop,{	If (target = parent)		Break	If (Mod(A_Index,10) = 0)		output .= A_Index ": " parent ", fitness: " fitness(parent, target) "`n"	bestFit := 0	Loop, %C%	  If ((fitness := fitness(spawn := mutate(parent), target)) > bestFit)		  bestSpawn := spawn , bestFit := fitness	parent := bestFit > fitness(parent, target) ? bestSpawn : parent	iter := A_Index}output .= parent ", " iterMsgBox, % outputExitApp mutate(parent) {	local	output, replaceChar, newChar	output := ""	Loop, %targetLen%	{		Random, replaceChar, 0, 9		If (replaceChar != 0)			output .= SubStr(parent, A_Index, 1)		else		{			Random, newChar, 1, 27			output .= possibilities_%newChar%		}	}	Return output} fitness(string, target) {	totalFit := 0	Loop, % StrLen(string)		If (SubStr(string, A_Index, 1) = SubStr(target, A_Index, 1))			totalFit++	Return totalFit}`

Output:

```10: DETRNNKR IAQPFLNVKZ AMXEASEL, fitness: 14
20: METKNNKS IL PALLKKE A XEASEL, fitness: 20
30: METHGNKS IT PSXLKKE A XEASEL, fitness: 23
40: METHGNKS IT IS LKKE A EEASEL, fitness: 25
50: METHGNKS IT IS LKKE A WEASEL, fitness: 26
60: METHGNKS IT IS LKKE A WEASEL, fitness: 26
70: METHGNKS IT IS LIKE A WEASEL, fitness: 27
METHINKS IT IS LIKE A WEASEL, 72
```

## AWK

I apply the rate to each character in each generated child. The number of generations required seems to be really sensitive to the rate. I used the default seeding in GNU awk to obtain the results below. I suspect the algorithm used to generate the pseudo-random numbers may also influence the rapidity of convergence but I haven't investigated that yet. The output shown was obtained using GNU Awk 3.1.5. BusyBox v1.20.0.git also works but using the same rate generates 88 generations before converging.

` #!/bin/awk -ffunction randchar(){return substr(charset,randint(length(charset)+1),1)}function mutate(gene,rate    ,l,newgene){newgene = ""for (l=1; l < 1+length(gene); l++){if (rand() < rate)   newgene = newgene randchar()else   newgene = newgene substr(gene,l,1)}return newgene}function fitness(gene,target  ,k,fit){fit = 0for (k=1;k<1+length(gene);k++){if (substr(gene,k,1) == substr(target,k,1)) fit = fit + 1}return fit}function randint(n){return int(n * rand())}function evolve(){     maxfit = fitness(parent,target)     oldfit = maxfit     maxj = 0     for (j=1; j < D; j++){         child[j] = mutate(parent,mutrate)         fit[j] = fitness(child[j],target)         if (fit[j] > maxfit) {            maxfit = fit[j]            maxj = j            }          }     if (maxfit > oldfit) parent = child[maxj]     } BEGIN{target = "METHINKS IT IS LIKE A WEASEL"charset = " ABCDEFGHIJKLMNOPQRSTUVWXYZ"mutrate = 0.10if (ARGC > 1) mutrate = ARGV[1]lenset = length(charset)C = 100D = C + 1parent = ""for (j=1; j < length(target)+1; j++) {     parent = parent randchar()     }print "target: " targetprint "fitness of target: " fitness(target,target)print "initial parent: " parentgens = 0while (parent != target){      evolve()      gens = gens + 1      if (gens % 10 == 0) print "after " gens " generations,","new parent: " parent," with fitness: " fitness(parent,target)      }print "after " gens " generations,"," evolved parent: " parent}  `

Output:

```# ./awkevolution .08998
target: METHINKS IT IS LIKE A WEASEL
fitness of target: 28
initial parent: EGVCODUCLCILXFXEPNHAMNV BP S
after 10 generations, new parent: EGTSIDKS IT XFXXIKHAANUDEW S  with fitness: 11
after 20 generations, new parent: MKTIIDKS IT IF XIKB A WEEWEL  with fitness: 20
after 30 generations, new parent: M TIIDKS IT IF LIKE A WENSEL  with fitness: 23
after 40 generations, new parent: METIIDKS IT IF LIKE A WEASEL  with fitness: 25
after 50 generations, new parent: METHIDKS IT IS LIKE A WEASEL  with fitness: 27
after 60 generations, new parent: METHINKS IT IS LIKE A WEASEL  with fitness: 28
after 60 generations,  evolved parent: METHINKS IT IS LIKE A WEASEL
#
```

## Batch File

` @echo offsetlocal enabledelayedexpansion set target=M E T H I N K S @ I T @ I S @ L I K E @ A @ W E A S E Lset chars=A B C D E F G H I J K L M N O P Q R S T U V W X Y Z @ set tempcount=0for %%i in (%target%) do (  set /a tempcount+=1  set target!tempcount!=%%i)call:parent echo  %target%echo  -------------------------------------------------------- :loopcall:fitness parentset currentfit=%errorlevel%if %currentfit%==28 goto endecho %parent% - %currentfit% [%attempts%]set attempts=0 :innerloopset /a attempts+=1title Attemps - %attempts%call:mutate %parent%call:fitness tempparentset newfit=%errorlevel%if %newfit% gtr %currentfit% (  set tempcount=0  set "parent="  for %%i in (%tempparent%) do (    set /a tempcount+=1    set parent!tempcount!=%%i    set parent=!parent! %%i  )  goto loop)goto innerloop :endecho %parent% - %currentfit% [%attempts%]echo Done.exit /b :parentset "parent="for /l %%i in (1,1,28) do (  set /a charchosen=!random! %% 27 + 1  set tempcount=0  for %%j in (%chars%) do (    set /a tempcount+=1    if !charchosen!==!tempcount! (      set parent%%i=%%j      set parent=!parent! %%j    )  ))exit /b :fitnessset fitness=0set array=%1for /l %%i in (1,1,28) do if !%array%%%i!==!target%%i! set /a fitness+=1exit /b %fitness% :mutateset tempcount=0set returnarray=tempparentset "%returnarray%="for %%i in (%*) do (  set /a tempcount+=1  set %returnarray%!tempcount!=%%i  set %returnarray%=!%returnarray%! %%i)set /a tomutate=%random% %% 28 + 1set /a mutateto=%random% %% 27 + 1set tempcount=0for %%i in (%chars%) do (  set /a tempcount+=1  if %mutateto%==!tempcount! (    set %returnarray%!tomutate!=%%i  ))set "%returnarray%="for /l %%i in (1,1,28) do set %returnarray%=!%returnarray%! !%returnarray%%%i!exit /b `
Output:

Sample Output:

``` M E T H I N K S @ I T @ I S @ L I K E @ A @ W E A S E L
--------------------------------------------------------
R S T L U M F Q Y B T L G P L Q T B F C B X F S X S H Y - 3 []
R S T L I M F Q Y B T L G P L Q T B F C B X F S X S H Y - 4 [9]
R S T L I M F Q Y B T L G S L Q T B F C B X F S X S H Y - 5 [49]
R E T L I M F Q Y B T L G S L Q T B F C B X F S X S H Y - 6 [2]
R E T L I M F Q Y B T L G S L Q T B F C B X F S X S H L - 7 [18]
R E T L I M F Q Y B T L G S L Q T B F C B X W S X S H L - 8 [5]
R E T L I M F Q Y B T @ G S L Q T B F C B X W S X S H L - 9 [13]
R E T L I M F Q Y B T @ G S L L T B F C B X W S X S H L - 10 [114]
R E T L I M K Q Y B T @ G S L L T B F C B X W S X S H L - 11 [9]
R E T L I M K Q Y B T @ G S @ L T B F C B X W S X S H L - 12 [17]
R E T L I M K S Y B T @ G S @ L T B F C B X W S X S H L - 13 [53]
R E T L I M K S Y I T @ G S @ L T B F C B X W S X S H L - 14 [20]
R E T L I M K S @ I T @ G S @ L T B F C B X W S X S H L - 15 [121]
R E T L I M K S @ I T @ G S @ L T B F C B X W S X S E L - 16 [86]
R E T L I M K S @ I T @ G S @ L T B F C B X W E X S E L - 17 [115]
R E T H I M K S @ I T @ G S @ L T B F C B X W E X S E L - 18 [54]
R E T H I M K S @ I T @ G S @ L T B F @ B X W E X S E L - 19 [121]
R E T H I M K S @ I T @ G S @ L T B F @ B X W E A S E L - 20 [207]
M E T H I M K S @ I T @ G S @ L T B F @ B X W E A S E L - 21 [5]
M E T H I M K S @ I T @ G S @ L I B F @ B X W E A S E L - 22 [163]
M E T H I M K S @ I T @ G S @ L I B E @ B X W E A S E L - 23 [84]
M E T H I M K S @ I T @ G S @ L I K E @ B X W E A S E L - 24 [31]
M E T H I N K S @ I T @ G S @ L I K E @ B X W E A S E L - 25 [432]
M E T H I N K S @ I T @ I S @ L I K E @ B X W E A S E L - 26 [85]
M E T H I N K S @ I T @ I S @ L I K E @ A X W E A S E L - 27 [144]
M E T H I N K S @ I T @ I S @ L I K E @ A @ W E A S E L - 28 [227]
Done.
```

## BBC BASIC

`      target\$ = "METHINKS IT IS LIKE A WEASEL"      parent\$ = "IU RFSGJABGOLYWF XSMFXNIABKT"      mutation_rate = 0.5      children% = 10       DIM child\$(children%)       REPEAT        bestfitness = 0        bestindex% = 0        FOR index% = 1 TO children%          child\$(index%) = FNmutate(parent\$, mutation_rate)          fitness = FNfitness(target\$, child\$(index%))          IF fitness > bestfitness THEN            bestfitness = fitness            bestindex% = index%          ENDIF        NEXT index%         parent\$ = child\$(bestindex%)        PRINT parent\$      UNTIL parent\$ = target\$      END       DEF FNfitness(text\$, ref\$)      LOCAL I%, F%      FOR I% = 1 TO LEN(text\$)        IF MID\$(text\$, I%, 1) = MID\$(ref\$, I%, 1) THEN F% += 1      NEXT      = F% / LEN(text\$)       DEF FNmutate(text\$, rate)      LOCAL C%      IF rate > RND(1) THEN        C% = 63+RND(27)        IF C% = 64 C% = 32        MID\$(text\$, RND(LEN(text\$)), 1) = CHR\$(C%)      ENDIF      = text\$`

## C

`#include <stdio.h>#include <stdlib.h>#include <string.h> const char target[] = "METHINKS IT IS LIKE A WEASEL";const char tbl[] = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "; #define CHOICE (sizeof(tbl) - 1)#define MUTATE 15#define COPIES 30 /* returns random integer from 0 to n - 1 */int irand(int n){	int r, rand_max = RAND_MAX - (RAND_MAX % n);	while((r = rand()) >= rand_max);	return r / (rand_max / n);} /* number of different chars between a and b */int unfitness(const char *a, const char *b){	int i, sum = 0;	for (i = 0; a[i]; i++)		sum += (a[i] != b[i]);	return sum;} /* each char of b has 1/MUTATE chance of differing from a */void mutate(const char *a, char *b){	int i;	for (i = 0; a[i]; i++)		b[i] = irand(MUTATE) ? a[i] : tbl[irand(CHOICE)]; 	b[i] = '\0';} int main(){	int i, best_i, unfit, best, iters = 0;	char specimen[COPIES][sizeof(target) / sizeof(char)]; 	/* init rand string */	for (i = 0; target[i]; i++)		specimen[0][i] = tbl[irand(CHOICE)];	specimen[0][i] = 0; 	do {		for (i = 1; i < COPIES; i++)			mutate(specimen[0], specimen[i]); 		/* find best fitting string */		for (best_i = i = 0; i < COPIES; i++) {			unfit = unfitness(target, specimen[i]);			if(unfit < best || !i) {				best = unfit;				best_i = i;			}		} 		if (best_i) strcpy(specimen[0], specimen[best_i]);		printf("iter %d, score %d: %s\n", iters++, best, specimen[0]);	} while (best); 	return 0;}`
output
`iter 0, score 26: WKVVYFJUHOMQJNZYRTEQAGDVXKYCiter 1, score 25: WKVVTFJUHOMQJN YRTEQAGDVSKXCiter 2, score 25: WKVVTFJUHOMQJN YRTEQAGDVSKXCiter 3, score 24: WKVVTFJUHOMQJN YRTEQAGDVAKFC...iter 221, score 1: METHINKSHIT IS LIKE A WEASELiter 222, score 1: METHINKSHIT IS LIKE A WEASELiter 223, score 0: METHINKS IT IS LIKE A WEASEL`

## C++

`#include <string>#include <cstdlib>#include <iostream>#include <cassert>#include <algorithm>#include <vector>#include <ctime> std::string allowed_chars = " ABCDEFGHIJKLMNOPQRSTUVWXYZ"; // class selection contains the fitness function, encapsulates the// target string and allows access to it's length. The class is only// there for access control, therefore everything is static. The// string target isn't defined in the function because that way the// length couldn't be accessed outside.class selection{public:  // this function returns 0 for the destination string, and a  // negative fitness for a non-matching string. The fitness is  // calculated as the negated sum of the circular distances of the  // string letters with the destination letters.  static int fitness(std::string candidate)  {    assert(target.length() == candidate.length());     int fitness_so_far = 0;     for (int i = 0; i < target.length(); ++i)    {      int target_pos = allowed_chars.find(target[i]);      int candidate_pos = allowed_chars.find(candidate[i]);      int diff = std::abs(target_pos - candidate_pos);      fitness_so_far -= std::min(diff, int(allowed_chars.length()) - diff);    }     return fitness_so_far;  }   // get the target string length  static int target_length() { return target.length(); }private:  static std::string target;}; std::string selection::target = "METHINKS IT IS LIKE A WEASEL"; // helper function: cyclically move a character through allowed_charsvoid move_char(char& c, int distance){  while (distance < 0)    distance += allowed_chars.length();  int char_pos = allowed_chars.find(c);  c = allowed_chars[(char_pos + distance) % allowed_chars.length()];} // mutate the string by moving the characters by a small random// distance with the given probabilitystd::string mutate(std::string parent, double mutation_rate){  for (int i = 0; i < parent.length(); ++i)    if (std::rand()/(RAND_MAX + 1.0) < mutation_rate)    {      int distance = std::rand() % 3 + 1;      if(std::rand()%2 == 0)        move_char(parent[i], distance);      else        move_char(parent[i], -distance);    }  return parent;} // helper function: tell if the first argument is less fit than the// secondbool less_fit(std::string const& s1, std::string const& s2){  return selection::fitness(s1) < selection::fitness(s2);} int main(){  int const C = 100;   std::srand(time(0));   std::string parent;  for (int i = 0; i < selection::target_length(); ++i)  {    parent += allowed_chars[std::rand() % allowed_chars.length()];  }   int const initial_fitness = selection::fitness(parent);   for(int fitness = initial_fitness;      fitness < 0;      fitness = selection::fitness(parent))  {    std::cout << parent << ": " << fitness << "\n";    double const mutation_rate = 0.02 + (0.9*fitness)/initial_fitness;    std::vector<std::string> childs;    childs.reserve(C+1);     childs.push_back(parent);    for (int i = 0; i < C; ++i)      childs.push_back(mutate(parent, mutation_rate));     parent = *std::max_element(childs.begin(), childs.end(), less_fit);  }  std::cout << "final string: " << parent << "\n";}`

Example output:

```BBQYCNLDIHG   RWEXN PNGFTCMS: -203
ECPZEOLCHFJBCXTXFYLZQPDDQ KP: -177
HBSBGMKEEIM BUTUGWKWNRCGSZNN: -150
GBRFGNKDAINX TVRITIZPSBERXTH: -129
JEUFILLDDGNZCWYRIWFWSUAERZUI: -120
JESGILIGDJOZCWXRIWFVSXZESXXI: -109
JCSHILIIDIOZCTZOIUIVVXZEUVXI: -93
KDSHHLJIDIOZER LIUGXVXXFWW I: -76
KDSHGNMIDIOZHR LIUHXWXWFWW L: -69
LDSHHNMLDIOZKR LGSEXWXWFYV L: -59
LDSHHNMNDIOYKU LGSEXY WFYV M: -55
LCSHHNMLDHR IT LGSEZY WFYSBM: -44
LCSHHNMNBIR IT LGSEZY WFASBM: -36
LCSHHNMQBIQ JT LGQEZY WFASBM: -33
LCSIHNMRBIS JT LGQE Y WFASBM: -30
LESIHNMSBIS JR LGQE Y WFASBM: -27
LESIJNMSBIS JR LHOE A WFASBM: -21
LERIJNJSBIS JR LHOF A WFASEM: -19
LERIJNJSBIS JR LHLF A WFASEM: -16
NERIJNJS IS JR LHLF A WFASEM: -14
NERIJNJS IS JS LHLF A WFASEM: -13
NERIJNKS IS JS LHLF A WFASEM: -12
NERIJNKS IS JS LHKF A WFASEM: -11
NERIJNKS IS JS LHKF A WFASEM: -11
NERIJNKS IS JS LHKF A WEASEM: -10
NERIJNKS IS JS LHKF A WEASEM: -10
NERIJNKS IS JS LHKF A WEASEL: -9
NERIJNKS IS JS LHKF A WEASEL: -9
NETIJNKS IS JS LHKF A WEASEL: -7
NETIJNKS IS JS LHKF A WEASEL: -7
NETIJNKS IT JS LHKF A WEASEL: -6
NETIINKS IT JS LHKF A WEASEL: -5
NETIINKS IT JS LHKE A WEASEL: -4
NETHINKS IT JS LHKE A WEASEL: -3
NETHINKS IT JS LIKE A WEASEL: -2
NETHINKS IT JS LIKE A WEASEL: -2
NETHINKS IT JS LIKE A WEASEL: -2
NETHINKS IT JS LIKE A WEASEL: -2
NETHINKS IT JS LIKE A WEASEL: -2
NETHINKS IT JS LIKE A WEASEL: -2
METHINKS IT JS LIKE A WEASEL: -1
METHINKS IT JS LIKE A WEASEL: -1
METHINKS IT JS LIKE A WEASEL: -1
final string: METHINKS IT IS LIKE A WEASEL
```

## C#

Works with: C# version 3+
`using System;using System.Collections.Generic;using System.Linq; static class Program {    static Random Rng = new Random((int)DateTime.Now.Ticks);     static char NextCharacter(this Random self) {        const string AllowedChars = " ABCDEFGHIJKLMNOPQRSTUVWXYZ";        return AllowedChars[self.Next() % AllowedChars.Length];    }     static string NextString(this Random self, int length) {        return String.Join("", Enumerable.Repeat(' ', length)            .Select(c => Rng.NextCharacter()));    }     static int Fitness(string target, string current) {        return target.Zip(current, (a, b) => a == b ? 1 : 0).Sum();    }     static string Mutate(string current, double rate) {        return String.Join("", from c in current               select Rng.NextDouble() <= rate ? Rng.NextCharacter() : c);    }     static void Main(string[] args) {        const string target = "METHINKS IT IS LIKE A WEASEL";        const int C = 100;        const double P = 0.05;         // Start with a random string the same length as the target.        string parent = Rng.NextString(target.Length);         Console.WriteLine("START:       {0,20} fitness: {1}",             parent, Fitness(target, parent));        int i = 0;         while (parent != target) {            // Create C mutated strings + the current parent.            var candidates = Enumerable.Range(0, C + 1)                .Select(n => n > 0 ? Mutate(parent, P) : parent);             // select the fittest            parent = candidates.OrderByDescending(c => Fitness(target, c)).First();             ++i;            Console.WriteLine("     #{0,6} {1,20} fitness: {2}",                 i, parent, Fitness(target, parent));        }         Console.WriteLine("END: #{0,6} {1,20}", i, parent);    }}`

Example output:

```START:       PACQXJB CQPWEYKSVDCIOUPKUOJY fitness: 0
#     1 PALQXJB CQPWEYKSVDCIOUPEUOJY fitness: 1
#     2 PALQXJB CQPWEYKSVDEIOUPEUOJY fitness: 2
#     3 PALQXJB CQPWEYKSVDE OUPEUOJY fitness: 3
#     4 MALQOJB CQPWEYKSVDE OUPEUOJY fitness: 4
#     5 MALQOJB CQPWEYKSVKE OUPEUOJY fitness: 5
#     6 MALQOJB CQPWEYKLVKE OUPEUOES fitness: 7
#     7 MALQOJB CQPWEYKLVKE OUPEAOES fitness: 8
#     8 M LQOJB CQPWEYKLVKE OUPEAOES fitness: 8
#     9 M LQOJB CQPWEYKL KE OUPEAOES fitness: 8
#    10 M LHOJB CQPWEYKL KE OUPEAOES fitness: 9
#    11 M LHOJB CQPWEYKL KE OGYEAOEL fitness: 10
#    12 M LHOJB CQP EYKL KE OGYEAOEL fitness: 11
#    13 M THOJB CQP EYKL KE OGYEAOEL fitness: 12
#    14 M THOJB CQP ESKL KE OGYEAOEL fitness: 13
#    15 M THOJB CQP ESKL KE AGYEAOEL fitness: 14
#    16 M THHJBSCQP ESKL KE AGYEAOEL fitness: 15
#    17 M THHJBSCQP ES L KE AGYEAOEL fitness: 16
#    18 MXTHHJBSCQP ES L KE AGYEASEL fitness: 17
#    19 MXTHHJBSCOT ES L KE AGYEASEL fitness: 18
#    20 MXTHHJBSCOT ES L KE AGYEASEL fitness: 18
#    21 METHHJBSCOT GS L KE ACYEASEL fitness: 19
#    22 METHIJBSCOT GS L KE ACYEASEL fitness: 20
#    23 METHILBSCOT GS L KE ACYEASEL fitness: 20
#    24 METHILBSCOT GS L KE ACWEASEL fitness: 21
#    25 METHILBS OT GS LBKE ACWEASEL fitness: 22
#    26 METHILBS OT GS LBKE ACWEASEL fitness: 22
#    27 METHILBS OT IS LBKE ACWEASEL fitness: 23
#    28 METHILBS OT IS LBKE ACWEASEL fitness: 23
#    29 METHILBS OT IS LBKE ACWEASEL fitness: 23
#    30 METHILBS CT IS LPKE ACWEASEL fitness: 23
#    31 METHILBS CT IS LPKE ACWEASEL fitness: 23
#    32 METHILBS CT IS LPKE A WEASEL fitness: 24
#    33 METHILBS ET IS LPKE A WEASEL fitness: 24
#    34 METHILBS ET IS LPKE A WEASEL fitness: 24
#    35 METHILBS ET IS LPKE A WEASEL fitness: 24
#    36 METHILBS ET IS LPKE A WEASEL fitness: 24
#    37 METHILBS IT IS LPKE A WEASEL fitness: 25
#    38 METHILBS IT IS LPKE A WEASEL fitness: 25
#    39 METHILBS IT IS LPKE A WEASEL fitness: 25
#    40 METHILBS IT IS LPKE A WEASEL fitness: 25
#    41 METHILBS IT IS LPKE A WEASEL fitness: 25
#    42 METHILBS IT IS LPKE A WEASEL fitness: 25
#    43 METHINBS IT IS LPKE A WEASEL fitness: 26
#    44 METHINBS IT IS LPKE A WEASEL fitness: 26
#    45 METHINBS IT IS LPKE A WEASEL fitness: 26
#    46 METHINBS IT IS LIKE A WEASEL fitness: 27
#    47 METHINBS IT IS LIKE A WEASEL fitness: 27
#    48 METHINBS IT IS LIKE A WEASEL fitness: 27
#    49 METHINBS IT IS LIKE A WEASEL fitness: 27
#    50 METHINBS IT IS LIKE A WEASEL fitness: 27
#    51 METHINBS IT IS LIKE A WEASEL fitness: 27
#    52 METHINBS IT IS LIKE A WEASEL fitness: 27
#    53 METHINBS IT IS LIKE A WEASEL fitness: 27
#    54 METHINBS IT IS LIKE A WEASEL fitness: 27
#    55 METHINBS IT IS LIKE A WEASEL fitness: 27
#    56 METHINBS IT IS LIKE A WEASEL fitness: 27
#    57 METHINBS IT IS LIKE A WEASEL fitness: 27
#    58 METHINBS IT IS LIKE A WEASEL fitness: 27
#    59 METHINBS IT IS LIKE A WEASEL fitness: 27
#    60 METHINBS IT IS LIKE A WEASEL fitness: 27
#    61 METHINBS IT IS LIKE A WEASEL fitness: 27
#    62 METHINKS IT IS LIKE A WEASEL fitness: 28
END: #    62 METHINKS IT IS LIKE A WEASEL
```

## Ceylon

`import ceylon.random { 	DefaultRandom} shared void run() { 	value mutationRate = 0.05;	value childrenPerGeneration = 100;	value target = "METHINKS IT IS LIKE A WEASEL";	value alphabet = {' ', *('A'..'Z')};	value random = DefaultRandom(); 	value randomLetter => random.nextElement(alphabet); 	function fitness(String a, String b) =>			count {for([c1, c2] in zipPairs(a, b)) c1 == c2}; 	function mutate(String string) =>			String {				for(letter in string) 				if(random.nextFloat() < mutationRate) 				then randomLetter 				else letter			}; 	function makeCopies(String string) =>			{for(i in 1..childrenPerGeneration) mutate(string)}; 	function chooseFittest(String+ children) =>			children			.map((String element) => element->fitness(element, target))			.max(increasingItem)			.key; 	variable value parent = String {for(i in 1..target.size) randomLetter};	variable value generationCount = 0;	function display() => print("``generationCount``: ``parent``"); 	display();	while(parent != target) {		parent = chooseFittest(parent, *makeCopies(parent));		generationCount++;		display();	} 	print("mutated into target in ``generationCount`` generations!"); }`

## Clojure

Define the evolution parameters (values here per Wikipedia article), with a couple of problem constants.

`(def c 100)  ;number of children in each generation(def p 0.05) ;mutation probability (def target "METHINKS IT IS LIKE A WEASEL")(def tsize (count target)) (def alphabet " ABCDEFGHIJLKLMNOPQRSTUVWXYZ")`

Now the major functions. fitness simply counts the number of characters matching the target.

`(defn fitness [s] (count (filter true? (map = s target))))(defn perfectly-fit? [s] (= (fitness s) tsize)) (defn randc [] (rand-nth alphabet))(defn mutate [s] (map #(if (< (rand) p) (randc) %) s))`

Finally evolve. At each generation, print the generation number, the parent, and the parent's fitness.

`(loop [generation 1, parent (repeatedly tsize randc)]  (println generation, (apply str parent), (fitness parent))  (if-not (perfectly-fit? parent)    (let [children (repeatedly c #(mutate parent))          fittest (apply max-key fitness parent children)]      (recur (inc generation), fittest))))`

## COBOL

For testing purposes, you can comment out the first two sentences in the CONTROL-PARAGRAPH and the program will then use the same sequence of pseudo-random numbers on each run.

`identification division.program-id. evolutionary-program.data division.working-storage section.01  evolving-strings.    05 target                pic a(28)        value 'METHINKS IT IS LIKE A WEASEL'.    05 parent                pic a(28).    05 offspring-table.        10 offspring         pic a(28)            occurs 50 times.01  fitness-calculations.    05 fitness               pic 99.    05 highest-fitness       pic 99.    05 fittest               pic 99.01  parameters.    05 character-set         pic a(27)        value 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '.    05 size-of-generation    pic 99        value 50.    05 mutation-rate         pic 99        value 5.01  counters-and-working-variables.    05 character-position    pic 99.    05 randomization.        10 random-seed       pic 9(8).        10 random-number     pic 99.        10 random-letter     pic 99.    05 generation            pic 999.    05 child                 pic 99.    05 temporary-string      pic a(28).procedure division.control-paragraph.    accept random-seed from time.    move function random(random-seed) to random-number.    perform random-letter-paragraph,    varying character-position from 1 by 1    until character-position is greater than 28.    move temporary-string to parent.    move zero to generation.    perform output-paragraph.    perform evolution-paragraph,    varying generation from 1 by 1    until parent is equal to target.    stop run.evolution-paragraph.    perform mutation-paragraph varying child from 1 by 1    until child is greater than size-of-generation.    move zero to highest-fitness.    move 1 to fittest.    perform check-fitness-paragraph varying child from 1 by 1    until child is greater than size-of-generation.    move offspring(fittest) to parent.    perform output-paragraph.output-paragraph.    display generation ': ' parent.random-letter-paragraph.    move function random to random-number.    divide random-number by 3.80769 giving random-letter.    add 1 to random-letter.    move character-set(random-letter:1)    to temporary-string(character-position:1).mutation-paragraph.    move parent to temporary-string.    perform character-mutation-paragraph,    varying character-position from 1 by 1    until character-position is greater than 28.    move temporary-string to offspring(child).character-mutation-paragraph.    move function random to random-number.    if random-number is less than mutation-rate    then perform random-letter-paragraph.check-fitness-paragraph.    move offspring(child) to temporary-string.    perform fitness-paragraph.fitness-paragraph.    move zero to fitness.    perform character-fitness-paragraph,    varying character-position from 1 by 1    until character-position is greater than 28.    if fitness is greater than highest-fitness    then perform fittest-paragraph.character-fitness-paragraph.    if temporary-string(character-position:1) is equal to    target(character-position:1) then add 1 to fitness.fittest-paragraph.    move fitness to highest-fitness.    move child to fittest.`
Output:
```000: YZPLJKKFEZTWMSGAPVMUZBKBLLRS
001: YZPLJKKFEZTWMSGAPVMUZBKBLLRS
002: YZPLJKKFEZTWMS APVMUZBKBLLRS
003: JZPLJKKFEZTWMS AIVMUZBKBLLRS
004: JZPLJKKFEZTWMS AIVBUABKBLLRS
005: JZPLJKKFEZTWIS AIVBUABKBLLRS
006: JZPLJKKFEZTWIS AIVBUABKBLLRS
007: MVPLXKKFECTWIS AIVBUABKBLLRS
008: MVPLXKKSECTWIS AIVBUABKBLLRS
009: MVPLCKKSUCTWIS AIVBUABKBLLRS
010: MVPLCKKSUCTJIS LIVBVABKBLLRS
011: MVPLCKKSUCTJIS LIVBVABKBLSRS
012: MVPLCKKSUCTJIS LIVBQABKBLSRS
013: MVPLCKKSUCTJIS LIVBQABKBLSRS
014: MEPLCKKSUCTJIS LIVBQABKBLSRS
015: MEPVCKKSUCTJIS LIVBFABKBLSRS
016: MEPVCKKSUCTJIS LIVBFABKBLSRE
017: MEPVCKKSUCTJIS LIVBFABKBLSEE
018: MEPVCKKSUCTJIS LIVBFABWBLSEE
019: MEPVCKKSUCTJIS LIVBFABWBLSEE
020: MEPXCKKSUCTJIS LIVBFABWBLSEE
021: MEPXCKKSUCTJIS LIVBFABWBLSEE
022: MEPXCKKSUSTJIS LIVBFABWBLSEE
023: MEPXCKKSUSTJIS LIVBFABWBASEE
024: MEPXCKKSUSTJIS LIVEFABWBASEM
025: MEPXCKKSUSTJIS LIVEFABWEASEM
026: MEPXCKKSUSTJIS LIVEFABWEASEM
027: MEPXCKKSUITJIS LIVEFABWEASEM
028: MEPXCNKSUITJIS LIVEFABWEASEM
029: MEPXCNKSUITJIS LIVEFABWEASEM
030: MEPXCNKS ITJIS LIVEFABWEASEM
031: MEPXCNKS ITJIS LIVEFABWEASEM
032: MEPXCNKS ITJIS LIVEFABWEASEM
033: MEPXCNKS ITJIS LIVEFABWEASEM
034: MEPXCNKS ITNIS LIVEFABWEASEM
035: METICNKS ITNIS LIVEYABWEASEM
036: METICNKS ITNIS LIVEYABWEASEM
037: METICNKS ITMIS LIVEYABWEASEM
038: METIHNKS ITMIS LIVEYABWEASEM
039: METIHNKS ITMIS LIVEYABWEASEM
040: METIHNKS ITMIS LIKEYABWEASEM
041: METIHNKS IT IS LIKEYABWEASEM
042: METIHNKS IT IS LIKEYABWEASEM
043: METIHNKS IT IS LIKEPABWEASEM
044: METIHNKS IT IS LIKEPABWEASEM
045: METHHNKS IT IS LIKEPABWEASEM
046: METHHNKS IT IS LIKEPABWEASEM
047: METHHNKS IT IS LIKEPABWEASEM
048: METHHNKS IT IS LIKEPABWEASEM
049: METHHNKS IT IS LIKEPABWEASEM
050: METHHNKS IT IS LIKEPABWEASEM
051: METHHNKS IT IS LIKEPABWEASEM
052: METHHNKS IT IS LIKEPABWEASEL
053: METHHNKS IT IS LIKEPABWEASEL
054: METHHNKS IT IS LIKEPA WEASEL
055: METHHNKS IT IS LIKEPA WEASEL
056: METHHNKS IT IS LIKEPA WEASEL
057: METHINKS IT IS LIKEPA WEASEL
058: METHINKS IT IS LIKEPA WEASEL
059: METHINKS IT IS LIKECA WEASEL
060: METHINKS IT IS LIKECA WEASEL
061: METHINKS IT IS LIKEAA WEASEL
062: METHINKS IT IS LIKEAA WEASEL
063: METHINKS IT IS LIKEAA WEASEL
064: METHINKS IT IS LIKETA WEASEL
065: METHINKS IT IS LIKETA WEASEL
066: METHINKS IT IS LIKETA WEASEL
067: METHINKS IT IS LIKETA WEASEL
068: METHINKS IT IS LIKETA WEASEL
069: METHINKS IT IS LIKETA WEASEL
070: METHINKS IT IS LIKETA WEASEL
071: METHINKS IT IS LIKETA WEASEL
072: METHINKS IT IS LIKETA WEASEL
073: METHINKS IT IS LIKETA WEASEL
074: METHINKS IT IS LIKETA WEASEL
075: METHINKS IT IS LIKETA WEASEL
076: METHINKS IT IS LIKETA WEASEL
077: METHINKS IT IS LIKETA WEASEL
078: METHINKS IT IS LIKETA WEASEL
079: METHINKS IT IS LIKETA WEASEL
080: METHINKS IT IS LIKETA WEASEL
081: METHINKS IT IS LIKETA WEASEL
082: METHINKS IT IS LIKETA WEASEL
083: METHINKS IT IS LIKETA WEASEL
084: METHINKS IT IS LIKETA WEASEL
085: METHINKS IT IS LIKETA WEASEL
086: METHINKS IT IS LIKETA WEASEL
087: METHINKS IT IS LIKETA WEASEL
088: METHINKS IT IS LIKETA WEASEL
089: METHINKS IT IS LIKETA WEASEL
090: METHINKS IT IS LIKETA WEASEL
091: METHINKS IT IS LIKETA WEASEL
092: METHINKS IT IS LIKETA WEASEL
093: METHINKS IT IS LIKETA WEASEL
094: METHINKS IT IS LIKETA WEASEL
095: METHINKS IT IS LIKETA WEASEL
096: METHINKS IT IS LIKETA WEASEL
097: METHINKS IT IS LIKETA WEASEL
098: METHINKS IT IS LIKETA WEASEL
099: METHINKS IT IS LIKETA WEASEL
100: METHINKS IT IS LIKETA WEASEL
101: METHINKS IT IS LIKETA WEASEL
102: METHINKS IT IS LIKETA WEASEL
103: METHINKS IT IS LIKETA WEASEL
104: METHINKS IT IS LIKETA WEASEL
105: METHINKS IT IS LIKETA WEASEL
106: METHINKS IT IS LIKETA WEASEL
107: METHINKS IT IS LIKETA WEASEL
108: METHINKS IT IS LIKETA WEASEL
109: METHINKS IT IS LIKETA WEASEL
110: METHINKS IT IS LIKETA WEASEL
111: METHINKS IT IS LIKETA WEASEL
112: METHINKS IT IS LIKETA WEASEL
113: METHINKS IT IS LIKETA WEASEL
114: METHINKS IT IS LIKETA WEASEL
115: METHINKS IT IS LIKETA WEASEL
116: METHINKS IT IS LIKETA WEASEL
117: METHINKS IT IS LIKETA WEASEL
118: METHINKS IT IS LIKETA WEASEL
119: METHINKS IT IS LIKETA WEASEL
120: METHINKS IT IS LIKETA WEASEL
121: METHINKS IT IS LIKETA WEASEL
122: METHINKS IT IS LIKETA WEASEL
123: METHINKS IT IS LIKETA WEASEL
124: METHINKS IT IS LIKETA WEASEL
125: METHINKS IT IS LIKETA WEASEL
126: METHINKS IT IS LIKETA WEASEL
127: METHINKS IT IS LIKEDA WEASEL
128: METHINKS IT IS LIKEDA WEASEL
129: METHINKS IT IS LIKEDA WEASEL
130: METHINKS IT IS LIKEKA WEASEL
131: METHINKS IT IS LIKEKA WEASEL
132: METHINKS IT IS LIKEKA WEASEL
133: METHINKS IT IS LIKEKA WEASEL
134: METHINKS IT IS LIKEKA WEASEL
135: METHINKS IT IS LIKEKA WEASEL
136: METHINKS IT IS LIKEKA WEASEL
137: METHINKS IT IS LIKEKA WEASEL
138: METHINKS IT IS LIKEKA WEASEL
139: METHINKS IT IS LIKEKA WEASEL
140: METHINKS IT IS LIKEKA WEASEL
141: METHINKS IT IS LIKEKA WEASEL
142: METHINKS IT IS LIKEKA WEASEL
143: METHINKS IT IS LIKEKA WEASEL
144: METHINKS IT IS LIKEKA WEASEL
145: METHINKS IT IS LIKEKA WEASEL
146: METHINKS IT IS LIKEKA WEASEL
147: METHINKS IT IS LIKEKA WEASEL
148: METHINKS IT IS LIKEKA WEASEL
149: METHINKS IT IS LIKEKA WEASEL
150: METHINKS IT IS LIKEKA WEASEL
151: METHINKS IT IS LIKEKA WEASEL
152: METHINKS IT IS LIKEKA WEASEL
153: METHINKS IT IS LIKEKA WEASEL
154: METHINKS IT IS LIKEKA WEASEL
155: METHINKS IT IS LIKEKA WEASEL
156: METHINKS IT IS LIKEKA WEASEL
157: METHINKS IT IS LIKEKA WEASEL
158: METHINKS IT IS LIKEKA WEASEL
159: METHINKS IT IS LIKEKA WEASEL
160: METHINKS IT IS LIKEKA WEASEL
161: METHINKS IT IS LIKEKA WEASEL
162: METHINKS IT IS LIKEKA WEASEL
163: METHINKS IT IS LIKEKA WEASEL
164: METHINKS IT IS LIKEHA WEASEL
165: METHINKS IT IS LIKEHA WEASEL
166: METHINKS IT IS LIKEHA WEASEL
167: METHINKS IT IS LIKEHA WEASEL
168: METHINKS IT IS LIKEHA WEASEL
169: METHINKS IT IS LIKEHA WEASEL
170: METHINKS IT IS LIKEYA WEASEL
171: METHINKS IT IS LIKEYA WEASEL
172: METHINKS IT IS LIKEYA WEASEL
173: METHINKS IT IS LIKEYA WEASEL
174: METHINKS IT IS LIKEYA WEASEL
175: METHINKS IT IS LIKEYA WEASEL
176: METHINKS IT IS LIKEYA WEASEL
177: METHINKS IT IS LIKEYA WEASEL
178: METHINKS IT IS LIKEYA WEASEL
179: METHINKS IT IS LIKEYA WEASEL
180: METHINKS IT IS LIKEYA WEASEL
181: METHINKS IT IS LIKEYA WEASEL
182: METHINKS IT IS LIKEYA WEASEL
183: METHINKS IT IS LIKEYA WEASEL
184: METHINKS IT IS LIKEYA WEASEL
185: METHINKS IT IS LIKEYA WEASEL
186: METHINKS IT IS LIKEYA WEASEL
187: METHINKS IT IS LIKEYA WEASEL
188: METHINKS IT IS LIKEYA WEASEL
189: METHINKS IT IS LIKEZA WEASEL
190: METHINKS IT IS LIKEZA WEASEL
191: METHINKS IT IS LIKEZA WEASEL
192: METHINKS IT IS LIKEZA WEASEL
193: METHINKS IT IS LIKEZA WEASEL
194: METHINKS IT IS LIKE A WEASEL```

## ColdFusion

` <Cfset theString = 'METHINKS IT IS LIKE A WEASEL'><cfparam name="parent" default=""><Cfset theAlphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "><Cfset fitness = 0><Cfset children = 3><Cfset counter = 0> <Cfloop from="1" to="#children#" index="child">  <Cfparam name="child#child#" default="">  <Cfparam name="fitness#child#" default=0></Cfloop> <Cfloop condition="fitness lt 1">   <Cfset oldparent = parent>  <Cfset counter = counter + 1>   <cfloop from="1" to="#children#" index="child">    <Cfset thischild = ''>     <Cfloop from="1" to="#len(theString)#" index="i">      <cfset Mutate = Mid(theAlphabet, RandRange(1, 28), 1)>      <cfif fitness eq 0>        <Cfset thischild = thischild & mutate>      <Cfelse>         <Cfif Mid(theString, i, 1) eq Mid(variables["child" & child], i, 1)>          <Cfset thischild = thischild & Mid(variables["child" & child], i, 1)>        <Cfelse>                <cfset MutateChance = 1/fitness>          <Cfset MutateChanceRand = rand()>          <Cfif MutateChanceRand lte MutateChance>             <Cfset thischild = thischild & mutate>          <Cfelse>            <Cfset thischild = thischild & Mid(variables["child" & child], i, 1)>          </Cfif>        </Cfif>       </cfif>     </Cfloop>     <Cfset variables["child" & child] = thischild> </cfloop>   <cfloop from="1" to="#children#" index="child">    <Cfset thisChildFitness = 0>    <Cfloop from="1" to="#len(theString)#" index="i">      <Cfif Mid(variables["child" & child], i, 1) eq Mid(theString, i, 1)>        <Cfset thisChildFitness = thisChildFitness + 1>      </Cfif>    </Cfloop>     <Cfset variables["fitness" & child] = (thisChildFitness)/len(theString)>     <Cfif variables["fitness" & child] gt fitness>      <Cfset fitness = variables["fitness" & child]>      <Cfset parent = variables["child" & child]>    </Cfif>   </cfloop>   <Cfif parent neq oldparent>    <Cfoutput>###counter# #numberformat(fitness*100, 99)#% fit: #parent#<br></Cfoutput><cfflush>  </Cfif> </Cfloop> `
```#1 7% fit: VOPJOBSYPTTUNYYSAFHTPJUIAIL
#2 18% fit: FQUFHEKPLXTQISYZZRIEVQWBHRC
#33 29% fit: M THILKORWP XSRVOLV GVIRVJHE
#34 36% fit: MEBHRNTSYPH IHTCHMH LGWBAFZ
#37 39% fit: MSTHIWKLIHU KSSLECR Z WGUMZE
#61 43% fit: METHINKA RT ZRQCEFVEAMWKZEBA
#62 50% fit: METHINKA GT RLQAOHVSAXWNAS A
#67 54% fit: MESHINKT IGBWSRLIEEAF WERYWH
#72 57% fit: METHINKE VT YBUJNRXRA W XSEL
#129 64% fit: METHINKS ITCIEHLPNB A YYAAPL
#156 68% fit: METHINKS IT IHIWJKY I W GSAL
#177 71% fit: METHINKS IT IS RIPRPA BEAVYN
#180 75% fit: METHINKS IT IS OI BAA TEABBL
#185 79% fit: METHINKS IT IS LIQEWA EEARLX
#197 82% fit: METHINKS IT IS LIKP OKWEASMU
#222 86% fit: METHINKS IT IS LIKESG WEALEH
#245 89% fit: METHINKS IT IS LIKEOA GEAQEL
#304 93% fit: METHINKS IT IS LIKE A WESSYL
#349 96% fit: METHINKS IT IS LIKE A WEASOL
#360 100% fit: METHINKS IT IS LIKE A WEASEL
```

## Common Lisp

`(defun fitness (string target)  "Closeness of string to target; lower number is better"  (loop for c1 across string        for c2 across target        count (char/= c1 c2))) (defun mutate (string chars p)  "Mutate each character of string with probablity p using characters from chars"  (dotimes (n (length string))    (when (< (random 1.0) p)      (setf (aref string n) (aref chars (random (length chars))))))  string) (defun random-string (chars length)  "Generate a new random string consisting of letters from char and specified length"  (do ((n 0 (1+ n))       (str (make-string length)))      ((= n length) str)    (setf (aref str n) (aref chars (random (length chars)))))) (defun evolve-string (target string chars c p)  "Generate new mutant strings, and choose the most fit string"  (let ((mutated-strs (list string)))    (dotimes (n c)      (push (mutate (copy-seq string) chars p) mutated-strs))    (reduce #'(lambda (s0 s1)                (if (< (fitness s0 target)                       (fitness s1 target))                    s0                    s1))            mutated-strs))) (defun evolve-gens (target c p)  (let ((chars " ABCDEFGHIJKLMNOPQRSTUVWXYZ"))    (do ((parent (random-string chars (length target))                 (evolve-string target parent chars c p))         (n 0 (1+ n)))        ((string= target parent) (format t "Generation ~A: ~S~%" n parent))      (format t "Generation ~A: ~S~%" n parent))))`

Sample output:

```CL-USER> (evolve-gens "METHINKS IT IS LIKE A WEASEL" 100 0.05)
Generation 0: "IFNGR ACQNOAWQZYHNIUPLRHTPCP"
Generation 1: "IUNGRHAC NOAWQZYHNIUPLRHTPCP"
Generation 2: "IUNGRHAC YO WQZYHNIUPLRHTPCP"
Generation 3: "IUNGRHKC YO WQZYHNIUPLJHTPRP"
Generation 4: "IUNGRHKC IO WQZYHVIUPLVHTPRP"
Generation 5: "IUNGRNKC IO WQZYHVIUPLVHNPRP"
Generation 6: "IUNGRNKC IO WQZYHVIUPLVHNPRP"
Generation 7: "IENGRNKC IO WQZYHVIUPLVHNPRP"
Generation 8: "IENGRNKC IO WQZYHVEURLVHNPRP"
Generation 9: "IENMRNKC IO WQZYHVE RLVHNPRP"
Generation 10: "IENMRNKC IO WQZYHVE RLVHNPRP"
Generation 11: "IENMRNKC IO WQZYHVE RLVHNPRP"
Generation 12: "IEZMRNKC IO WQZYAVE RLVHNSRP"
Generation 13: "IEZMRNKC IO WQZYIVE RLVHNSRP"
Generation 14: "IEZMRNKC IO WQZYIKE RLVHNSRP"
Generation 15: "IEZMRNKC IO WQZYIKE RLVHNSRL"
Generation 16: "IEZ INKC IZ WQZYIKE RLVHNSRL"
Generation 17: "IET INKC IZ WQZYIKE RLVHNSRL"
Generation 18: "IET INKC IZ WQZYIKE RLVHNSEL"
Generation 19: "IET INKC IZ WQZ IKE RLVHASEL"
Generation 20: "GET INKC IZ WSZ IKE RLVHASEL"
Generation 21: "GET INKC IZ WSZ IKE RLVHASEL"
Generation 22: "GET INKC IZ WSZ IKE RLVHASEL"
Generation 23: "GET INKC IZ ISZ IKE RLVHASEL"
Generation 24: "GET INKC IZ ISZ IKE RLWHASEL"
Generation 25: "MET INKC IZ ISZ IKE OLWHASEL"
Generation 26: "MET INKC IZ ISZ IKE OLWHASEL"
Generation 27: "MET INKC IZ ISZ IKE ALWHASEL"
Generation 28: "MET INKC IZ ISZ IKE A WHASEL"
Generation 29: "METHINKC IZ ISZ IKE A WHASEL"
Generation 30: "METHINKC IZ ISZ IKE A WHASEL"
Generation 31: "METHINKC IZ ISZ IKE A WHASEL"
Generation 32: "METHINKC IZ ISZ IKE A WEASEL"
Generation 33: "METHINKC IZ ISZ IKE A WEASEL"
Generation 34: "METHINKC IZ ISZ IKE A WEASEL"
Generation 35: "METHINKC IT ISZLIKD A WEASEL"
Generation 36: "METHINKC IT ISZLIKD A WEASEL"
Generation 37: "METHINKC IT ISZLIKD A WEASEL"
Generation 38: "METHINKC IT ISZLIKD A WEASEL"
Generation 39: "METHINKC IT ISZLIKD A WEASEL"
Generation 40: "METHINKC IT ISZLIKE A WEASEL"
Generation 41: "METHINKC IT IS LIKE A WEASEL"
Generation 42: "METHINKC IT IS LIKE A WEASEL"
Generation 43: "METHINKS IT IS LIKE A WEASEL"
```

Mutates one character at a time, with only on offspring each generation (which competes against the parent):

`(defun unfit (s1 s2)  (loop for a across s1	for b across s2 count(char/= a b))) (defun mutate (str alp n) ; n: number of chars to mutate  (let ((out (copy-seq str)))    (dotimes (i n) (setf (char out (random (length str)))			 (char alp (random (length alp)))))    out)) (defun evolve (changes alpha target)  (loop for gen from 1	with f2 with s2	with str = (mutate target alpha 100)	with fit = (unfit target str)	while (plusp fit) do	(setf s2 (mutate str alpha changes)	      f2 (unfit target s2))	(when (> fit f2)	  (setf str s2 fit f2)	  (format t "~5d: ~a (~d)~%" gen str fit)))) (evolve 1 " ABCDEFGHIJKLMNOPQRSTUVWXYZ" "METHINKS IT IS LIKE A WEASEL")`
outupt
`   44: DYZTOREXDML ZCEUCSHRVHBEPGJE (26)   57: DYZTOREXDIL ZCEUCSHRVHBEPGJE (25)   83: DYZTOREX IL ZCEUCSHRVHBEPGJE (24)   95: MYZTOREX IL ZCEUCSHRVHBEPGJE (23)  186: MYZTOREX IL ZCEUISHRVHBEPGJE (22)  208: MYZTOREX IL ZCEUISH VHBEPGJE (21)  228: MYZTOREX IL ZCEUISH VHBEPGEE (20)  329: MYZTOREX IL ZCEUIKH VHBEPGEE (19)  330: MYTTOREX IL ZCEUIKH VHBEPGEE (18)  354: MYTHOREX IL ZCEUIKH VHBEPGEE (17)  365: MYTHOREX IL ICEUIKH VHBEPGEE (16)  380: MYTHOREX IL ISEUIKH VHBEPGEE (15)  393: METHOREX IL ISEUIKH VHBEPGEE (14)  407: METHORKX IL ISEUIKH VHBEPGEE (13)  443: METHORKX IL ISEUIKH VHBEPSEE (12)  455: METHORKX IL ISEUIKE VHBEPSEE (11)  477: METHIRKX IL ISEUIKE VHBEPSEE (10)  526: METHIRKS IL ISEUIKE VHBEPSEE (9)  673: METHIRKS IL ISEUIKE VHBEPSEL (8)  800: METHINKS IL ISEUIKE VHBEPSEL (7)  875: METHINKS IL ISEUIKE AHBEPSEL (6)  941: METHINKS IL ISEUIKE AHBEASEL (5) 1175: METHINKS IT ISEUIKE AHBEASEL (4) 1214: METHINKS IT ISELIKE AHBEASEL (3) 1220: METHINKS IT IS LIKE AHBEASEL (2) 1358: METHINKS IT IS LIKE AHWEASEL (1) 2610: METHINKS IT IS LIKE A WEASEL (0)`

## D

`import std.stdio, std.random, std.algorithm, std.range, std.ascii; enum target = "METHINKS IT IS LIKE A WEASEL"d;enum C = 100;  // Number of children in each generation.enum P = 0.05; // Mutation probability.enum fitness = (dchar[] s) => target.zip(s).count!q{ a[0] != a[1] };dchar rnd() { return (uppercase ~ " ")[uniform(0, \$)]; }enum mut = (dchar[] s) => s.map!(a => uniform01 < P ? rnd : a).array; void main() {    auto parent = generate!rnd.take(target.length).array;    for (auto gen = 1; parent != target; gen++) {        // parent = parent.repeat(C).map!mut.array.max!fitness;        parent = parent.repeat(C).map!mut.array                 .minPos!((a, b) => a.fitness < b.fitness)[0];        writefln("Gen %2d, dist=%2d: %s", gen, parent.fitness, parent);    }}`
Output:
```Generation  0, dist=25: PTJNKPFVJFTDRSDVNUB ESJGU MF
Generation  1, dist=18: PEKNKNKSBFTDISDVIUB ESJEP MF
Generation  2, dist=12: NETVKNKS FTDISDLIUE EIJEPSEF
Generation  3, dist= 8: NETVONKS ITDISDLIUE AIWEASEF
Generation  4, dist= 8: NETVONKS ITDISDLIUE AIWEASEF
Generation  5, dist= 6: NETHONKS ITDIS LINE AIWEASEW
Generation  6, dist= 5: NETHINKS ITSIS LINE AIWEASEW
Generation  7, dist= 5: NETHINKS ITSIS LINE AIWEASEW
Generation  8, dist= 4: NETHINKS ITSIS LINE A WEASEW
Generation  9, dist= 3: METHINKS ITSIS LINE A WEASEW
Generation 10, dist= 3: METHINKS ITSIS LINE A WEASEW
Generation 11, dist= 3: METHINKS ITSIS LINE A WEASEW
Generation 12, dist= 2: METHINKS IT IS LINE A WEASEW
Generation 13, dist= 2: METHINKS IT IS LINE A WEASEW
Generation 14, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 15, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 16, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 17, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 18, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 19, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 20, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 21, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 22, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 23, dist= 1: METHINKS IT IS LIKE A WEASEW
Generation 24, dist= 0: METHINKS IT IS LIKE A WEASEL```

## E

`pragma.syntax("0.9")pragma.enable("accumulator") def target := "METHINKS IT IS LIKE A WEASEL"def alphabet := "ABCDEFGHIJKLMNOPQRSTUVWXYZ "def C := 100def RATE := 0.05 def randomCharString() {  return E.toString(alphabet[entropy.nextInt(alphabet.size())])} def fitness(string) {    return accum 0 for i => ch in string {      _ + (ch == target[i]).pick(1, 0)    }} def mutate(string, rate) {  return accum "" for i => ch in string {    _ + (entropy.nextDouble() < rate).pick(randomCharString(), E.toString(ch))  }} def weasel() {  var parent := accum "" for _ in 1..(target.size()) { _ + randomCharString() }  var generation := 0   while (parent != target) {    println(`\$generation \$parent`)    def copies := accum [] for _ in 1..C { _.with(mutate(parent, RATE)) }    var best := parent    for c in copies {      if (fitness(c) > fitness(best)) {        best := c      }    }    parent := best    generation += 1  }  println(`\$generation \$parent`)} weasel()`

## EchoLisp

` (require 'sequences)(define ALPHABET (list->vector  ["A" .. "Z"] ))(vector-push ALPHABET " ") (define (fitness source target) ;; score >=0, best is 0	(for/sum  [(s source)(t target)]		(if (= s t) 0 1))) (define (mutate source rate)	(for/string [(s source)]		(if (< (random) rate) [ALPHABET (random 27)] s))) (define (select parent target rate copies (copy) (score))	(define best (fitness parent target))	(define selected parent)	(for [(i copies)]		(set! copy (mutate parent rate))		(set! score (fitness copy target))		(when (< score  best)			(set! selected copy)			(set! best  score)))	selected ) (define MUTATION_RATE 0.05) ;; 5% chances to change(define COPIES 100)(define TARGET "METHINKS IT IS LIKE A WEASEL") (define (task (rate MUTATION_RATE) (copies COPIES) (target TARGET) (score))	(define parent ;; random source		(for/string                 [(i (string-length target))] [ALPHABET (random 27)])) 	(for [(i (in-naturals))]		(set! score (fitness parent target))		(writeln i parent 'score score)		#:break (zero? score)		(set! parent (select parent target rate copies))		)) `
Output:
```(task)
0     "TNCEKMNVYOW NSMSZ BZDODMMAXE"     score     26
1     "TNCEKBNVYOW NSMSZ AZDODMMAEE"     score     25
2     "TNCEKINVYOW NSMSZKEZDODMMAEE"     score     23
3     "TNCEKIKVYOW NSMSZKEZDODMMAEE"     score     22
4     "TNCEKIKVYOW NSMSZKEZDOWMMAEE"     score     21
5     "TNCEKIKVYOW NSMSZKEZDOWMMAEE"     score     21
6     "MNCEKIKVYOW NSMSZKEZSOWMMAEE"     score     20
7     "MNCEKIKAYOE NSMLZKEZSOWMMAEE"     score     19
8     "MNCEKIKAYOE NSMLZKEZS WMMAEE"     score     18
9     "MNCEKIKAYOE ISMLZKEZS WMMAEE"     score     17
10     "MECEKIKAYBE ISMLZKEZS WMMAEE"     score     16
11     "MECEKLKAYBE ISMLZKE S WMMAEE"     score     15
12     "METEKZKAYBE ISMLZKE S WMMAEE"     score     14
13     "METEKZKAYBE ISMLZKE S WMMSEE"     score     13
14     "METEIZKAYBE ISMLZKE S WMMSEH"     score     12
15     "METEIZKAYBE ISMLZKE S WMMSEH"     score     12
16     "METHIZKAYBE ISMLZKE S WMMSEH"     score     11
17     "METHIZKAYBE ISMLZKE S WMASEH"     score     10
18     "METHIZKAYBE ISMLZKE S WMASEH"     score     10
[...]
67     "METHINKS RT ISMLIKE A WEASEL"     score     2
68     "METHINKS RT ISMLIKE A WEASEL"     score     2
69     "METHINKS RT ISMLIKE A WEASEL"     score     2
70     "METHINKS RT ISMLIKE A WEASEL"     score     2
71     "METHINKS RT ISMLIKE A WEASEL"     score     2
72     "METHINKS RT IS LIKE A WEASEL"     score     1
73     "METHINKS RT IS LIKE A WEASEL"     score     1
74     "METHINKS RT IS LIKE A WEASEL"     score     1
75     "METHINKS IT IS LIKE A WEASEL"     score     0
```

## Elena

ELENA 3.4 :

`import system'routines.import extensions.import extensions'text. const literal Target = "METHINKS IT IS LIKE A WEASEL".const literal AllowedCharacters = " ABCDEFGHIJKLMNOPQRSTUVWXYZ". const int C = 100.const real P = 0.05r. rnd = randomGenerator. randomChar     = AllowedCharacters[rnd nextInt(AllowedCharacters length)]. extension evoHelper{    randomString        = 0 till:self repeat(:x)( randomChar ); summarize(StringWriter new); literal.     fitnessOf:s        = self zip:s by(:a:b)( (a == b)iif(1,0) ); summarize(Integer new); toInt.     mutate : p        = self selectBy(:ch)( (rnd nextReal <= p) iif(randomChar,ch) ); summarize(StringWriter new); literal.} class EvoAlgorithm :: Enumerator{    object theTarget.    object theCurrent.    object theVariantCount.     constructor new : s of:count    [        theTarget := s.        theVariantCount := count toInt.    ]     get = theCurrent.     bool next    [        if (nil == theCurrent)            [ theCurrent := theTarget length; randomString. ^ true ].         if (theTarget == theCurrent)            [ ^ false ].         auto variants := Array new:theVariantCount; populate(:x)( theCurrent mutate:P ).         theCurrent := variants sort(:a:b)( a fitnessOf:Target > b fitnessOf:Target ); getAt:0.         ^ true.    ]      reset    [        theCurrent := nil.    ]     enumerable => theTarget.                          } public program[    var attempt := Integer new.    EvoAlgorithm new:Target of:C; forEach(:current)    [        console             printPaddingLeft(10,"#",attempt append:1);            printLine(" ",current," fitness: ",current fitnessOf:Target).    ].     console readChar]`
Output:
```        #1 WYHOOITVJKCPTOOTEVZJUNLCFDCV fitness: 0
#2 WYHOOITV KCPTOOTEVZJUNLCFDCV fitness: 1
#3 WYHOOITS KCPTOCTEVZ UNLCFDCV fitness: 3
#4 WYHO ITS KCPTO TEVZ UELCFDCV fitness: 4
#5 WYGO ITS DC ZO TEVZ UELCFDCV fitness: 5
#6 WYGO ITS DC ZO TEVZ UELCADCV fitness: 6
#7 WYGO ITS DT ZO TEVZ UELCADCV fitness: 7
#8 WYGOIITS DT ZO TEVZ LELCADRV fitness: 8
#9 WYGOIITS DT ZO TEVZ LELCADRL fitness: 9
#10 WYTOIITS HT ZZ TEVZ LEQCADRL fitness: 10
#11 WYTOIITS HT ZZ IEKZ LEQCADRL fitness: 11
#12 WYTOIITS HT ZZ IEKZ LEQCADEL fitness: 12
#13 WYTOIITS HT ZZ IEKZ LEQCASEL fitness: 13
#14 WYTOIIKS HT BZ IEKZ LEQCASEL fitness: 14
...
#34 METHINKS GT BS LGKE AEWGASEL fitness: 23
#35 METHINKS GT BS LIKE AEWGASEL fitness: 24
#36 METHINKS GT BS LIKE AEWGASEL fitness: 24
#37 METHINKS GT BS LIKE AEWGASEL fitness: 24
#38 METHINKS GT BS LIKE AEWGASEL fitness: 24
#39 METHINKS GT IS LIKE AEWYASEL fitness: 25
#40 METHINKS GT IS LIKE AEWYASEL fitness: 25
#41 METHINKS GT IS LIKE AEWEASEL fitness: 26
#42 METHINKS GT IS LIKE AEWEASEL fitness: 26
#43 METHINKS GT IS LIKE AEWEASEL fitness: 26
#44 METHINKS GT IS LIKE AEWEASEL fitness: 26
#45 METHINKS GT IS LIKE AEWEASEL fitness: 26
#46 METHINKS GT IS LIKE AEWEASEL fitness: 26
#47 METHINKS GT IS LIKE AEWEASEL fitness: 26
...
#57 METHINKS GT IS LIKE A WEASEL fitness: 27
#58 METHINKS GT IS LIKE A WEASEL fitness: 27
#59 METHINKS GT IS LIKE A WEASEL fitness: 27
#60 METHINKS GT IS LIKE A WEASEL fitness: 27
#61 METHINKS GT IS LIKE A WEASEL fitness: 27
#62 METHINKS GT IS LIKE A WEASEL fitness: 27
#63 METHINKS GT IS LIKE A WEASEL fitness: 27
#64 METHINKS LT IS LIKE A WEASEL fitness: 27
#65 METHINKS LT IS LIKE A WEASEL fitness: 27
#66 METHINKS LT IS LIKE A WEASEL fitness: 27
#67 METHINKS LT IS LIKE A WEASEL fitness: 27
#68 METHINKS LT IS LIKE A WEASEL fitness: 27
#69 METHINKS LT IS LIKE A WEASEL fitness: 27
#70 METHINKS LT IS LIKE A WEASEL fitness: 27
#71 METHINKS IT IS LIKE A WEASEL fitness: 28
```

## Elixir

Works with: Elixir version 1.3

Print current gen and most fit offspring if more fit than parent.
Print the target and the total number of generations (iterations) it took to reach it.

`defmodule Log do  def show(offspring,i) do    IO.puts "Generation: #{i}, Offspring: #{offspring}"  end   def found({target,i}) do    IO.puts "#{target} found in #{i} iterations"  endend defmodule Evolution do  # char list from A to Z; 32 is the ord value for space.  @chars  [32 | Enum.to_list(?A..?Z)]   def select(target) do     (1..String.length(target)) # Creates parent for generation 0.      |> Enum.map(fn _-> Enum.random(@chars) end)       |> mutate(to_charlist(target),0)      |> Log.found  end   # w is used to denote fitness in population genetics.   defp mutate(parent,target,i) when target == parent, do: {parent,i}  defp mutate(parent,target,i) do    w = fitness(parent,target)     prev = reproduce(target,parent,mu_rate(w))     # Check if the most fit member of the new gen has a greater fitness than the parent.    if w < fitness(prev,target) do      Log.show(prev,i)      mutate(prev,target,i+1)    else      mutate(parent,target,i+1)    end  end   # Generate 100 offspring and select the one with the greatest fitness.   defp reproduce(target,parent,rate) do    [parent | (for _ <- 1..100, do: mutation(parent,rate))]      |> Enum.max_by(fn n -> fitness(n,target) end)  end   # Calculate fitness by checking difference between parent and offspring chars.   defp fitness(t,r) do    Enum.zip(t,r)      |> Enum.reduce(0, fn {tn,rn},sum -> abs(tn - rn) + sum end)       |> calc  end   # Generate offspring based on parent.   defp mutation(p,r) do    # Copy the parent chars, then check each val against the random mutation rate    Enum.map(p, fn n -> if :rand.uniform <= r, do: Enum.random(@chars), else: n end)  end   defp calc(sum),  do: 100 * :math.exp(sum/-10)  defp mu_rate(n), do: 1   - :math.exp(-(100-n)/400)end Evolution.select("METHINKS IT IS LIKE A WEASEL")`
Output:
```Generation: 0, Offspring: AFOSPRRLTLF CQKYFIGUMEUVBLRN
Generation: 1, Offspring: HFOMJRRESLL FQKYQRGUM UVBLRN
Generation: 2, Offspring: HFOMCRLIDLL FDKYQRGNM UVBLIN
Generation: 3, Offspring: HFOMCOLIDQL FDKYQRG M UVBLIP
Generation: 4, Offspring: HFOMCOLVLRL FD YYRG M UEBLIP
Generation: 5, Offspring: HFOMCOLVLRL FS YYNH M UEBXJP
Generation: 6, Offspring: KFOMCOLVLRL FS YYNH C UEBXJP
Generation: 7, Offspring: EFOFCOCVLFT FV YCNH C UEBMJP
Generation: 8, Offspring: EFWFCOCV FTBFV YCSH C UEBMJP
Generation: 9, Offspring: EFWFJOCZ FTBRV DCMH C UEBMJP
Generation: 11, Offspring: PFSFJOCL FVBRV DCMH C UEBJJP
Generation: 12, Offspring: PFSDJYCL LV RK DKMH C UEBJJR
Generation: 13, Offspring: IFSDJYCP LV MK DKMH C UEBSJR
Generation: 14, Offspring: IFSDJTIP LV MK DKMH C UEBSGR
Generation: 15, Offspring: IFSDJTIO JV MK SKMH C UEBSGR
Generation: 16, Offspring: IFSKIJIO JV MK DKMH C UEBSGG
Generation: 19, Offspring: IFSJIJIP JV MK DKIH C UEBSGH
Generation: 20, Offspring: IFSJIJIP JV MO DMIH C UEBSGH
Generation: 21, Offspring: IFWJDJIP JV IO EHJH C UEBSGH
Generation: 23, Offspring: IFWJDJIP JV IO SHJH A XEBSGH
Generation: 25, Offspring: IFWJDJIP JV IO SHJC A XEBSGH
Generation: 26, Offspring: IFWJKJIP JV IO LHJC A XEBOGH
Generation: 34, Offspring: IFTJKJIT JV IO LHJC A XEBOGH
Generation: 39, Offspring: IFTJKOIT JV IO LHJC A XEBOGH
Generation: 53, Offspring: IETJKOIT JV IO LHJC A XEBOGH
Generation: 60, Offspring: IETJKOIT JV IO LHJC A XEBOEG
Generation: 64, Offspring: IETJKOIT JV IO LHJF A XEBOEG
Generation: 68, Offspring: LETGKOIT JV IO LHJF A XEBOEG
Generation: 70, Offspring: LETGKOIT JV IS LHJF A XEBOBG
Generation: 76, Offspring: LETEKOIT JV IS LHJF A XEBOBN
Generation: 83, Offspring: LETHKOIT JV IS LHJF A XEBOFN
Generation: 90, Offspring: LBTHKOIT JV IS LHJF A XEBSFN
Generation: 92, Offspring: LBTHKOIT JV IS LHJF A XEBSFL
Generation: 93, Offspring: LBTHKOJT JV IS LHJF A XEBSFL
Generation: 123, Offspring: LETHKOJT JV IS LHJF A XEBSFL
Generation: 125, Offspring: LETHHOJT JV IS LHJF A XEBSFL
Generation: 135, Offspring: LETHHOJT JV IS LIJF A XEBSFL
Generation: 143, Offspring: LETHHOJT IV IS LIJF A XEBSFL
Generation: 161, Offspring: LETHHNJT IV IS LIJF A XEBSFL
Generation: 165, Offspring: METHHNJT IV IS LIJF A XEBSFL
Generation: 169, Offspring: METHHNKT IV IS LIJF A XEBSFL
Generation: 171, Offspring: METHHNKT IV IS LIJE A XEBSFL
Generation: 175, Offspring: METHHNKT IS IS LIJE A XEBSFL
Generation: 213, Offspring: METHHNKT IS IS LIKE A XEBSFL
Generation: 218, Offspring: METHINKT IS IS LIKE A XEBSFL
Generation: 234, Offspring: METHINKT IS IS LIKE A XEBSEL
Generation: 237, Offspring: METHINKT IS IS LIKE A XEASEL
Generation: 241, Offspring: METHINKT IS IS LIKE A WEASEL
Generation: 243, Offspring: METHINKT IT IS LIKE A WEASEL
Generation: 247, Offspring: METHINKS IT IS LIKE A WEASEL
METHINKS IT IS LIKE A WEASEL found in 248 iterations
```

## Erlang

`-module(evolution).-export([run/0]). -define(MUTATE, 0.05).-define(POPULATION, 100).-define(TARGET, "METHINKS IT IS LIKE A WEASEL").-define(MAX_GENERATIONS, 1000). run() -> evolve_gens(). evolve_gens() ->    Initial = random_string(length(?TARGET)),    evolve_gens(Initial,0,fitness(Initial)).evolve_gens(Parent,Generation,0) ->    io:format("Generation[~w]: Achieved the target: ~s~n",[Generation,Parent]);evolve_gens(Parent,Generation,_Fitness) when Generation == ?MAX_GENERATIONS ->    io:format("Reached Max Generations~nFinal string is ~s~n",[Parent]);evolve_gens(Parent,Generation,Fitness) ->    io:format("Generation[~w]: ~s, Fitness: ~w~n",              [Generation,Parent,Fitness]),    Child = evolve_string(Parent),    evolve_gens(Child,Generation+1,fitness(Child)). fitness(String) -> fitness(String, ?TARGET).fitness([],[]) -> 0;fitness([H|Rest],[H|Target]) -> fitness(Rest,Target);fitness([_H|Rest],[_T|Target]) -> 1+fitness(Rest,Target). mutate(String) -> mutate(String,[]).mutate([],Acc) -> lists:reverse(Acc);mutate([H|T],Acc) ->    case random:uniform() < ?MUTATE of        true ->            mutate(T,[random_character()|Acc]);        false ->            mutate(T,[H|Acc])    end. evolve_string(String) ->    evolve_string(String,?TARGET,?POPULATION,String).evolve_string(_,_,0,Child) -> Child;evolve_string(Parent,Target,Population,Best_Child) ->    Child = mutate(Parent),    case fitness(Child) < fitness(Best_Child) of        true ->            evolve_string(Parent,Target,Population-1,Child);        false ->            evolve_string(Parent,Target,Population-1,Best_Child)    end. random_character() ->    case random:uniform(27)-1 of        26  -> \$ ;        R -> \$A+R    end. random_string(Length) -> random_string(Length,[]).random_string(0,Acc) -> Acc;random_string(N,Acc) when N > 0 ->    random_string(N-1,[random_character()|Acc]).  `

## Euphoria

`constant table = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "function random_generation(integer len)    sequence s    s = rand(repeat(length(table),len))    for i = 1 to len do        s[i] = table[s[i]]    end for    return send function function mutate(sequence s, integer n)    for i = 1 to length(s) do        if rand(n) = 1 then            s[i] = table[rand(length(table))]        end if    end for    return send function function fitness(sequence probe, sequence target)    atom sum    sum = 0    for i = 1 to length(target) do        sum += power(find(target[i], table) - find(probe[i], table), 2)    end for    return sqrt(sum/length(target))end function constant target = "METHINKS IT IS LIKE A WEASEL", C = 30, MUTATE = 15sequence parent, specimeninteger iter, bestatom fit, best_fitparent = random_generation(length(target))iter = 0while not equal(parent,target) do    best_fit = fitness(parent, target)    printf(1,"Iteration: %3d, \"%s\", deviation %g\n", {iter, parent, best_fit})    specimen = repeat(parent,C+1)    best = C+1    for i = 1 to C do        specimen[i] = mutate(specimen[i], MUTATE)        fit = fitness(specimen[i], target)        if fit < best_fit then            best_fit = fit            best = i        end if    end for    parent = specimen[best]    iter += 1end whileprintf(1,"Finally, \"%s\"\n",{parent})`

Output:

```Iteration:   0, "HRGPWKOOARZL KTJEBPUYPTOLGDK", deviation 11.1002
Iteration:   1, "HRGPWKOOWRZLLKTJEBPUYPTOLGDK", deviation 9.40175
Iteration:   2, "HRGPOKOOWRZVLKTJEBPUYPTOLGDK", deviation 8.69113
Iteration:   3, "HRKPOKOOWRZVLKTJEBPUDPTOLGDB", deviation 7.46181
Iteration:   4, "HEKPOKOOWRZVLKTJEBPUDPTOLGDB", deviation 7.04577
Iteration:   5, "HEKPOKOOWRZVLKTJEBEUDPTOLGDB", deviation 6.73212
Iteration:   6, "HEKPOKOOWRZVLKTJEBEUDPTALGDB", deviation 6.50549
Iteration:   7, "HEKPOKOOWIZVLKTJEBEUDPTALGDB", deviation 6.27922
Iteration:   8, "HESPOKOOWIZVLKTJEBEUDPTALJDB", deviation 5.85845
Iteration:   9, "HESPOKOOWIZVLKTJEBEUIPTALJDJ", deviation 5.73212
...
Iteration: 201, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 202, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 203, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 204, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 205, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 206, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 207, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 208, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 209, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Iteration: 210, "METHINKS IT IT LIKE A WEASEL", deviation 0.188982
Finally, "METHINKS IT IS LIKE A WEASEL"
```

## F#

` //A functional implementation of Evolutionary algorithm//Nigel Galloway February 7th., 2018let G=System.Random 23let fitness n=Array.fold2(fun a n g->if n=g then a else a+1) 0 n ("METHINKS IT IS LIKE A WEASEL".ToCharArray())let alphabet="QWERTYUIOPASDFGHJKLZXCVBNM ".ToCharArray()let mutate (n:char[]) g=Array.iter(fun g->n.[g]<-alphabet.[G.Next()%27]) (Array.init g (fun _->G.Next()%(Array.length n)));nlet nextParent n g=List.init 500 (fun _->mutate (Array.copy n) g)|>List.minBy fitnesslet evolution n=let rec evolution n g=match fitness n with |0->(0,n)::g |l->evolution (nextParent n ((l/2)+1)) ((l,n)::g)                evolution n []let n = evolution (Array.init 28 (fun _->alphabet.[G.Next()%27])) `

Real: 00:00:00.021, CPU: 00:00:00.050, GC gen0: 1, gen1: 0
Length of n (37) is the number of generations including the original parent as follows:

```Length of n (37) is the number of generations including the original parent as follows:
(28, [|' '; 'V'; 'L'; 'D'; 'N'; 'Q'; 'A'; 'Z'; 'P'; 'A'; 'J'; 'A'; 'T'; 'C'; 'S'; 'I'; 'G'; 'H'; 'M'; 'Q'; 'M'; 'J'; 'Y'; 'L'; 'Q'; 'H'; 'S'; 'A'|])
(25, [|'D'; 'V'; 'L'; 'B'; 'N'; 'S'; 'A'; 'Z'; 'B'; 'A'; 'J'; 'Y'; 'T'; 'M'; 'U'; 'L'; 'G'; 'M'; 'M'; 'Q'; 'M'; ' '; 'Y'; 'L'; 'Q'; S'; 'X'; 'Y'|])
(23, [|'V'; 'E'; 'L'; 'A'; 'N'; 'S'; 'A'; 'Z'; 'P'; 'A'; 'J'; ' '; 'T'; 'M'; 'L'; 'L'; 'G'; 'D'; 'M'; 'Z'; 'S'; ' '; 'A'; 'L'; 'L'; 'S'; 'X'; 'Y'|])
(21, [|'V'; 'S'; 'L'; 'J'; 'N'; 'S'; 'A'; 'S'; 'P'; 'A'; 'J'; ' '; ' '; 'M'; 'L'; 'L'; 'G'; 'D'; 'E'; 'I'; 'A'; ' '; 'A'; 'L'; 'L'; 'S'; 'X'; 'Y'|])
(20, [|'V'; 'S'; 'E'; 'H'; 'N'; ' '; 'A'; 'S'; 'P'; 'S'; 'J'; ' '; 'Z'; 'P'; 'L'; 'L'; 'G'; 'B'; 'E'; 'Y'; 'A'; ' '; 'D'; 'H'; 'V'; 'S'; 'X'; 'Y'|])
(18, [|'V'; 'S'; 'K'; 'H'; 'N'; ' '; 'K'; 'S'; 'M'; 'S'; 'J'; ' '; 'I'; 'P'; 'V'; 'L'; 'D'; 'B'; 'E'; 'Y'; 'A'; ' '; 'X'; 'J'; 'V'; 'S'; 'X'; 'Y'|])
(16, [|'W'; 'S'; 'K'; 'H'; 'N'; ' '; 'K'; 'S'; 'M'; 'S'; 'D'; ' '; 'I'; 'S'; 'V'; 'L'; 'D'; 'T'; 'E'; ' '; 'A'; ' '; 'C'; 'J'; 'V'; 'S'; 'W'; 'Y'|])
(14, [|'W'; 'E'; 'K'; 'H'; 'X'; 'G'; 'K'; 'S'; 'M'; 'H'; 'D'; ' '; 'I'; 'S'; 'V'; 'L'; ' '; 'T'; 'E'; ' '; 'A'; ' '; 'C'; 'J'; 'R'; 'S'; 'W'; 'L'|])
(14, [|'W'; 'E'; 'E'; 'H'; 'I'; 'L'; 'K'; 'S'; 'M'; 'H'; 'D'; 'W'; 'I'; 'S'; 'O'; 'L'; 'M'; 'A'; 'E'; ' '; 'A'; ' '; 'Q'; 'J'; 'R'; 'S'; 'W'; 'L'|])
(13, [|'W'; 'E'; 'E'; 'H'; 'I'; 'L'; 'K'; 'S'; 'M'; 'H'; 'D'; 'W'; 'I'; 'S'; 'R'; 'L'; 'S'; 'A'; 'E'; ' '; 'A'; ' '; 'Q'; 'J'; 'Z'; 'S'; 'E'; 'L'|])
(12, [|'W'; 'E'; 'E'; 'H'; 'I'; 'L'; 'K'; 'S'; 'M'; 'H'; 'D'; 'O'; 'I'; 'S'; 'C'; 'L'; 'I'; 'Y'; 'E'; ' '; 'A'; ' '; 'J'; 'O'; 'R'; 'S'; 'E'; 'L'|])
(10, [|'B'; 'E'; 'A'; 'H'; 'I'; 'N'; 'K'; 'S'; 'M'; 'C'; 'T'; 'O'; 'I'; 'S'; 'C'; 'L'; 'I'; 'R'; 'E'; ' '; 'A'; ' '; 'J'; 'O'; 'R'; 'S'; 'E'; 'L'|])
(9, [|'M'; 'E'; 'A'; 'H'; 'I'; 'N'; 'K'; 'S'; 'N'; 'C'; 'T'; 'F'; 'I'; 'S'; 'C'; 'L'; 'I'; 'R'; 'E'; ' '; 'A'; ' '; 'K'; 'N'; 'R'; 'S'; 'E'; 'L'|])
(9, [|'M'; 'E'; 'A'; 'H'; 'I'; 'N'; 'K'; 'S'; 'T'; 'P'; 'T'; 'F'; 'I'; 'S'; 'C'; 'L'; 'I'; 'R'; 'E'; ' '; 'A'; ' '; 'K'; 'N'; 'P'; 'S'; 'E'; 'L'|])
(8, [|'M'; 'E'; 'N'; 'H'; 'I'; 'N'; 'K'; 'S'; 'L'; 'P'; 'T'; 'F'; 'I'; 'S'; 'Y'; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'H'; 'P'; 'S'; 'E'; 'L'|])
(8, [|'M'; 'E'; 'N'; 'H'; 'I'; 'N'; 'K'; 'S'; 'L'; 'E'; 'T'; 'F'; 'I'; 'R'; 'Y'; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'Q'; 'H'; 'A'; 'S'; 'E'; 'L'|])
(7, [|'M'; 'E'; ' '; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'E'; 'T'; 'F'; 'I'; 'K'; 'Y'; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'Q'; 'H'; 'A'; 'S'; 'E'; 'L'|])
(7, [|'M'; 'E'; ' '; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'E'; 'T'; 'F'; 'I'; 'K'; 'J'; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'Q'; 'H'; 'A'; 'S'; 'E'; 'L'|])
(6, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; 'F'; 'I'; 'K'; 'J'; 'L'; 'I'; 'D'; 'E'; ' '; 'A'; ' '; 'Q'; 'Z'; 'A'; 'S'; 'E'; 'L'|])
(5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'E'; 'J'; 'L'; 'I'; 'T'; 'E'; ' '; 'A'; ' '; 'X'; 'Z'; 'A'; 'S'; 'E'; 'L'|])
(5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'F'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; 'I'; 'L'; 'I'; 'T'; 'E'; ' '; 'A'; ' '; 'X'; 'Z'; 'A'; 'S'; 'E'; 'L'|])
(5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'F'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'T'; 'E'; ' '; 'A'; ' '; 'K'; 'Z'; 'A'; 'Z'; 'E'; 'L'|])
(5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'F'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'F'; 'E'; ' '; 'A'; ' '; 'K'; 'Z'; 'A'; 'P'; 'E'; 'L'|])
(5, [|'M'; 'E'; 'T'; 'H'; 'I'; 'R'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'F'; 'E'; ' '; 'A'; ' '; 'K'; 'Z'; 'A'; 'F'; 'E'; 'L'|])
(4, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'F'; 'E'; ' '; 'A'; ' '; 'K'; 'Z'; 'A'; 'F'; 'E'; 'L'|])
(3, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'J'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'F'; 'E'; 'L'|])
(3, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'J'; 'E'; ' '; 'A'; ' '; 'Y'; 'E'; 'A'; 'F'; 'E'; 'L'|])
(3, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'J'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'G'; 'E'; 'L'|])
(3, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'A'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'G'; 'E'; 'L'|])
(2, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'Q'; 'E'; 'L'|])
(2, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'Q'; 'E'; 'L'|])
(2, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'N'; 'E'; 'L'|])
(1, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'S'; 'E'; 'L'|])
(1, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'K'; 'E'; 'A'; 'S'; 'E'; 'L'|])
(1, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'N'; 'E'; 'A'; 'S'; 'E'; 'L'|])
(1, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'H'; 'E'; 'A'; 'S'; 'E'; 'L'|])
(0, [|'M'; 'E'; 'T'; 'H'; 'I'; 'N'; 'K'; 'S'; ' '; 'I'; 'T'; ' '; 'I'; 'S'; ' '; 'L'; 'I'; 'K'; 'E'; ' '; 'A'; ' '; 'W'; 'E'; 'A'; 'S'; 'E'; 'L'|])
```

## Factor

`USING: arrays formatting io kernel literals math prettyprintrandom sequences strings ;FROM: math.extras => ... ;IN: rosetta-code.evolutionary-algorithm CONSTANT: target "METHINKS IT IS LIKE A WEASEL"CONSTANT: mutation-rate 0.1CONSTANT: num-children 25CONSTANT: valid-chars    \$[ CHAR: A ... CHAR: Z >array { 32 } append ] : rand-char ( -- n )    valid-chars random ; : new-parent ( -- str )    target length [ rand-char ] replicate >string ; : fitness ( str -- n )    target [ = ] { } 2map-as sift length ; : mutate ( str rate -- str/str' )    [ random-unit > [ drop rand-char ] when ] curry map ; : next-parent ( str -- str/str' )    dup [ mutation-rate mutate ] curry num-children 1 - swap    replicate [ 1array ] dip append [ fitness ] supremum-by ; : print-parent ( str -- )    [ fitness pprint bl ] [ print ] bi ; : main ( -- )    0 new-parent    [ dup target = ]    [ next-parent dup print-parent [ 1 + ] dip ] until drop    "Finished in %d generations." printf ; MAIN: main`
Output:
```1 JWTBPZMHKOFFWDSBCLZUCFUAWUJ
2 JWTAPFMSKOFFWDSBCLZUCHUAWUJ
3 JWTAPSOSKOFFWDOBFLZ CHGAWUJ
...
14 MWTTISKS EFFWS LIKE JZGAWBKL
...
28 METHINKS IT IS LIKE A WEASEL
Finished in 298 generations.
```

## Fantom

` class Main{  static const Str target := "METHINKS IT IS LIKE A WEASEL"  static const Int C := 100     // size of population  static const Float p := 0.1f  // chance any char is mutated   // compute distance of str from target  static Int fitness (Str str)  {    Int sum := 0    str.each |Int c, Int index|    {      if (c != target[index]) sum += 1    }    return sum  }   // mutate given parent string  static Str mutate (Str str)  {    Str result := ""    str.size.times |Int index|     {      result += ((Float.random < p) ? randomChar() : str[index]).toChar    }    return result  }   // return a random char  static Int randomChar ()  {    "ABCDEFGHIJKLMNOPQRSTUVWXYZ "[Int.random(0..26)]  }   // make population by mutating parent and sorting by fitness  static Str[] makePopulation (Str parent)  {    Str[] result := [,]    C.times { result.add (mutate(parent)) }        result.sort |Str a, Str b -> Int| { fitness(a) <=> fitness(b) }    return result  }   public static Void main ()  {    Str parent := ""    target.size.times { parent += randomChar().toChar }     while (parent != target)    {      echo (parent)      parent = makePopulation(parent).first    }    echo (parent)  }} `

## Forth

Works with: 4tH version 3.60.0
`include lib/choose.4th                                       \ target strings" METHINKS IT IS LIKE A WEASEL" sconstant target 27 constant /charset                   \ size of characterset29 constant /target                    \ size of target string32 constant #copies                    \ number of offspring /target string charset                 \ characterset/target string this-generation         \ current generation and offspring/target #copies [*] string new-generation :this new-generation does> swap /target chars * + ;                                       \ generate a mutation: mutation charset /charset choose chars + [email protected] ;                                       \ print the current candidate: .candidate                           ( n1 n2 -- n1 f)  ." Generation " over 2 .r ." : " this-generation count type cr /target -1 [+] =;                                      \ test a candidate on                                        \ THE NUMBER of correct genes: test-candidate                       ( a -- a n)   dup target 0 >r >r                   ( a1 a2)  begin                                ( a1 a2)    [email protected]                                 ( a1 a2 n)  while                                ( a1 a2)                   over [email protected] over [email protected] =                  ( a1 a2 n)    r> r> rot if 1+ then >r 1- >r      ( a1 a2)    char+ swap char+ swap              ( a1+1 a2+1)  repeat                               ( a1+1 a2+1)  drop drop r> drop r>                 ( a n);                                       \ find the best candidate: get-candidate                        ( -- n)  #copies 0 >r >r                      ( --)  begin                                ( --)    [email protected]                                 ( n)  while                                ( --)    [email protected] 1- new-generation               ( a)    test-candidate r'@ over <          ( a n f)    if swap count this-generation place r> 1- swap r> drop >r >r    else drop drop r> 1- >r then       ( --)  repeat                               ( --)  r> drop r>                           ( n);                                       \ generate a new candidate: make-candidate                       ( a --)  dup charset count rot place          ( a1)  this-generation target >r            ( a1 a2 a3)  begin                                ( a1 a2 a3)    [email protected]                                 ( a1 a2 a3 n)  while                                ( a1 a2 a3)    over [email protected] over [email protected] =                  ( a1 a2 a3 f)    swap >r >r over r>                 ( a1 a2 a1 f)    if over [email protected] else mutation then      ( a1 a2 a1 c)    swap c! r> r> 1- >r                ( a1 a2 a3)    char+ rot char+ rot char+ rot      ( a1+1 a2+1 a3+1)  repeat                               ( a1+1 a2+1 a3+1)  drop drop drop r> drop               ( --);                                       \ make a whole new generation: make-generation #copies 0 do i new-generation make-candidate loop ;                                       \ weasel program: weasel  s"  ABCDEFGHIJKLMNOPQRSTUVWXYZ " 2dup  charset place                        \ initialize the characterset  this-generation place 0              \ initialize the first generation  begin                                \ start the program    1+ make-generation                 \ make a new generation    get-candidate .candidate           \ select the best candidate  until drop                           \ stop when we've found perfection; weasel`

Output:

```[email protected]:~> 4th cxq weasel1.4th
Generation  1: MUPHMOOXEIBGELPUZZEGXIVMELFL
Generation  2: MUBHIYDPKIQWYXSVLUEBH TYJMRL
Generation  3: MEVHIUTZDIVQSMRT KEDP GURBSL
Generation  4: MEWHIHKPKITBWSYVYKEXZ  ASBAL
Generation  5: MEVHIPKMRIT VSTSBKE R YNJWEL
Generation  6: MERHIIKQ IT OSNEUKE A TKCLEL
Generation  7: METHINKO IT  SXREKE A JDAIEL
Generation  8: METHINKS IT SSSVIKE A OIA EL
Generation  9: METHINKS IT ISICIKE A IGASEL
Generation 10: METHINKS IT ISITIKE A WZASEL
Generation 11: METHINKS IT ISACIKE A WEASEL
Generation 12: METHINKS IT ISKLIKE A WEASEL
Generation 13: METHINKS IT IS LIKE A WEASEL```

## Fortran

Works with: Fortran version 2003
`  !*************************************************************************************************** 	module evolve_routines !*************************************************************************************************** 	implicit none  	!the target string: 	character(len=*),parameter :: targ = 'METHINKS IT IS LIKE A WEASEL'  	contains !***************************************************************************************************  !******************************************************************** 	pure elemental function fitness(member) result(n) !******************************************************************** ! The fitness function.  The lower the value, the better the match. ! It is zero if they are identical. !********************************************************************  	implicit none 	integer :: n 	character(len=*),intent(in) :: member  	integer :: i  	n=0 	do i=1,len(targ) 		n = n + abs( ichar(targ(i:i)) - ichar(member(i:i))  ) 	end do  !******************************************************************** 	end function fitness !********************************************************************  !******************************************************************** 	pure elemental subroutine mutate(member,factor) !******************************************************************** ! mutate a member of the population. !********************************************************************  	implicit none 	character(len=*),intent(inout) :: member   !population member 	real,intent(in) :: factor                  !mutation factor  	integer,parameter :: n_chars = 27	!number of characters in set 	character(len=n_chars),parameter :: chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '  	real    :: rnd_val 	integer :: i,j,n  	n = len(member)  	do i=1,n 		rnd_val = rand() 		if (rnd_val<=factor) then   !mutate this element			 			rnd_val = rand() 			j = int(rnd_val*n_chars)+1   !an integer between 1 and n_chars 			member(i:i) = chars(j:j) 		end if 	end do  !********************************************************************	end subroutine mutate !********************************************************************  !*************************************************************************************************** 	end module evolve_routines !***************************************************************************************************  !*************************************************************************************************** 	program evolve !*************************************************************************************************** ! The main program !*************************************************************************************************** 	use evolve_routines  	implicit none  	!Tuning parameters: 	integer,parameter :: seed = 12345             !random number generator seed 	integer,parameter :: max_iter = 10000         !maximum number of iterations 	integer,parameter :: population_size = 200    !size of the population 	real,parameter    :: factor = 0.04            ![0,1] mutation factor 	integer,parameter :: iprint = 5               !print every iprint iterations  	!local variables: 	integer :: i,iter 	integer,dimension(1) :: i_best 	character(len=len(targ)),dimension(population_size) :: population  	!initialize random number generator: 	call srand(seed)  	!create initial population: 	! [the first element of the population will hold the best member] 	population(1) = 'PACQXJB CQPWEYKSVDCIOUPKUOJY'  !initial guess 	iter=0  	write(*,'(A10,A30,A10)') 'iter','best','fitness' 	write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))  	do   		iter = iter + 1 !iteration counter   		!write the iteration: 		if (mod(iter,iprint)==0) write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))  		!check exit conditions: 		if ( iter>max_iter .or. fitness(population(1))==0 ) exit  		!copy best member and mutate: 		population = population(1)	 		do i=2,population_size 			call mutate(population(i),factor)	 		end do  		!select the new best population member: 		! [the best has the lowest value] 		i_best = minloc(fitness(population)) 		population(1) = population(i_best(1))  	end do  	!write the last iteration: 	if (mod(iter,iprint)/=0) write(*,'(I10,A30,I10)') iter,population(1),fitness(population(1))  	if (iter>max_iter) then 		write(*,*) 'No solution found.' 	else 		write(*,*) 'Solution found.' 	end if  !*************************************************************************************************** 	end program evolve !*************************************************************************************************** `

The output is:

`       iter                          best   fitness         0  PACQXJB CQPWEYKSVDCIOUPKUOJY       459         5  PACDXJBRCQP EYKSVDK OAPKGOJY       278        10  PAPDJJBOCQP EYCDKDK A PHGQJF       177        15  PAUDJJBO FP FY VKBL A PEGQJF       100        20  PEUDJMOO KP FY IKLD A YECQJF        57        25  PEUHJMOT KU FS IKLD A YECQJL        35        30  PEUHJMIT KU GS LKJD A YEAQFL        23        35  MERHJMIT KT IS LHJD A YEASFL        15        40  MERHJMKS IT IS LIJD A WEASFL         7        45  MERHINKS IT IS LIJD A WEASFL         5        50  MERHINKS IT IS LIJD A WEASEL         4        55  MERHINKS IT IS LIKD A WEASEL         3        60  MESHINKS IT IS LIKD A WEASEL         2        65  MESHINKS IT IS LIKD A WEASEL         2        70  MESHINKS IT IS LIKE A WEASEL         1        75  METHINKS IT IS LIKE A WEASEL         0 `

## FreeBASIC

`' version 01-07-2018' compile with: fbc -s console Randomize TimerConst As UInteger children = 100Const As Double mutate_rate = 0.05 Function fitness(target As String, tmp As String) As UInteger     Dim As UInteger x, f     For x = 0 To Len(tmp) -1        If tmp[x] = target[x] Then f += 1    Next    Return f End Function Sub mutate(tmp As String, chars As String, mute_rate As Double)     If Rnd <= mute_rate Then        tmp[Int(Rnd * Len(tmp))] = chars[Int(Rnd * Len(chars))]    End If End Sub ' ------=< MAIN >=------ Dim As String target = "METHINKS IT IS LIKE A WEASEL"Dim As String chars  = " ABCDEFGHIJKLMNOPQRSTUVWXYZ"Dim As String parent, mutation()Dim As UInteger x, iter, f, fit(), best_fit, parent_fit For x = 1 To Len(target)    parent += Chr(chars[Int(Rnd * Len(chars))])Next f = fitness(target, parent)parent_fit = fbest_fit = f Print "iteration  best fit   Parent"Print "=========  ========   ============================"Print Using "     ####      ####   ";iter; best_fit;Print parent Do    iter += 1    ReDim mutation(1 To children),fit(1 To children)     For x = 1 To children        mutation(x) = parent        mutate(mutation(x), chars, mutate_rate)    Next     For x = 1 To children        If mutation(x) <> parent Then            f = fitness(target, mutation(x))            If best_fit < f Then                best_fit = f                fit(x) = f            Else                fit(x) = parent_fit            End If        End If    Next     If best_fit > parent_fit Then        For x = 1 To children            If fit(x) = best_fit Then                parent = mutation(x)                Print Using "     ####      ####   ";iter; best_fit;                Print parent            End If        Next    End If Loop Until parent = target ' empty keyboard bufferWhile InKey <> "" : WendPrint : Print "hit any key to end program"SleepEnd`
Output:
```iteration  best fit   Parent
=========  ========   ============================
0         2   VDHQATVSHHSVRFNAPFEGZARZGCZE
3         3   VEHQATVSHHSVRFNAPFEGZARZGCZE
5         4   VEHQATVSHHSVRFNAPFEGZAREGCZE
11         5   VEHQATKSHHSVRFNAPFEGZAREGCZE
19         6   VEHQATKSHHSVRFNAPFEGZAREGSZE
32         7   VEHQANKSHHSVRFNAPFEGZAREGSZE
36         8   VEHQANKSHHSVRFNAPFEGAAREGSZE
38         9   VEHQANKSHHTVRFNAPFEGAAREGSZE
39        10   VEHQANKSHHTVRFNAPFEGAAREGSEE
48        11   VEHHANKSHHTVRFNAPFEGAAREGSEE
53        12   VEHHANKSHITVRFNAPFEGAAREGSEE
73        13   VEHHINKSHITVRFNAPFEGAAREGSEE
81        14   VEHHINKSHITVRFNAPFEGAAWEGSEE
95        15   VEHHINKSHITVIFNAPFEGAAWEGSEE
96        16   VEHHINKSHITVIFNLPFEGAAWEGSEE
135        17   VETHINKSHITVIFNLPFEGAAWEGSEE
137        18   VETHINKSHITVISNLPFEGAAWEGSEE
152        19   VETHINKSHITVISNLPKEGAAWEGSEE
171        20   VETHINKSHITVISNLPKEGAAWEGSEL
174        21   VETHINKSHITVIS LPKEGAAWEGSEL
188        22   VETHINKSHITVIS LIKEGAAWEGSEL
213        23   VETHINKSHIT IS LIKEGAAWEGSEL
220        24   METHINKSHIT IS LIKEGAAWEGSEL
374        25   METHINKSHIT IS LIKE AAWEGSEL
378        26   METHINKSHIT IS LIKE A WEGSEL
555        27   METHINKS IT IS LIKE A WEGSEL
585        28   METHINKS IT IS LIKE A WEASEL```

## Go

I took the liberty to use `[]byte` for the "strings" mentioned in the task description. Go has a native string type, but in this case it was both easier and more efficient to work with byte slices and just convert to string when there was something to print.

`package main import (    "fmt"    "math/rand"    "time") var target = []byte("METHINKS IT IS LIKE A WEASEL")var set = []byte("ABCDEFGHIJKLMNOPQRSTUVWXYZ ")var parent []byte func init() {    rand.Seed(time.Now().UnixNano())    parent = make([]byte, len(target))    for i := range parent {        parent[i] = set[rand.Intn(len(set))]    }} // fitness:  0 is perfect fit.  greater numbers indicate worse fit.func fitness(a []byte) (h int) {    // (hamming distance)    for i, tc := range target {        if a[i] != tc {            h++        }    }    return} // set m to mutation of p, with each character of p mutated with probability rfunc mutate(p, m []byte, r float64) {    for i, ch := range p {        if rand.Float64() < r {            m[i] = set[rand.Intn(len(set))]        } else {            m[i] = ch        }    }} func main() {    const c = 20 // number of times to copy and mutate parent     copies := make([][]byte, c)    for i := range copies {        copies[i] = make([]byte, len(parent))    }     fmt.Println(string(parent))    for best := fitness(parent); best > 0; {        for _, cp := range copies {            mutate(parent, cp, .05)        }        for _, cp := range copies {            fm := fitness(cp)            if fm < best {                best = fm                copy(parent, cp)                fmt.Println(string(parent))            }        }    }}`
Output:
```HRVDKMXETOIOVSFMVHWKIY ZDXEY
HRVDKMXE OIOVSFMVHWKIY ZDWEY
HRVDKMXE OIOISFMVHWVIY ZDSEY
HRVDKMXE OIOISFMFHWVI  ZDSEL
HRVDKMXE OIOISFLFHWVI  ZDSEL
HRVDKMXE OIOISFLFHWVI  ZASEL
HRVDKMXS OIOISFLFHWVI  ZASEL
HRVHKMXS OIOISFLHHWVI  ZASEL
MRVHKMXS OHOISFLHHWVI  ZASEL
MRVHKMXS OTOISFLHHWVI  FASEL
MRVHKNXS OTOISFLHHWVI  FASEL
MRVHKNXS OTOISFLHHWVI  EASEL
MEVHKNXS OTOISFLHHWVI IEASEL
MEVHKNXS OTOISFLHHWVI WEASEL
METHKNXS OTOISFLHHWVI WEASEL
METHKNXS ZTOIS LHHWVI WEASEL
METHKNKS ZTOIS LHHWVI WEASEL
METHKNKS ZTOIS LHKWEI WEASEL
METHKNKS ZT IS LHKWEI WEASEL
METHKNKS ZT IS LHKEEI WEASEL
METHKNKS ZT IS LHKEEA WEASEL
METHKNKS ZT IS LHKE A WEASEL
METHKNKS ZT IS LIKE A WEASEL
METHINKS ZT IS LIKE A WEASEL
METHINKS IT IS LIKE A WEASEL```

Works with: GHC version 7.6.3
`import System.Randomimport Control.Monadimport Data.Listimport Data.Ordimport Data.Array showNum :: (Num a, Show a) => Int -> a -> StringshowNum w = until ((>w-1).length) (' ':) . show  replace :: Int -> a -> [a] -> [a]replace n c ls = take (n-1) ls ++ [c] ++ drop n ls target = "METHINKS IT IS LIKE A WEASEL"pfit = length targetmutateRate = 20popsize = 100charSet = listArray (0,26) \$ ' ': ['A'..'Z'] :: Array Int Char fitness = length . filter id . zipWith (==) target printRes i g = putStrLn \$     "gen:" ++ showNum 4 i ++ "  "     ++ "fitn:" ++ showNum 4  (round \$ 100 * fromIntegral s / fromIntegral pfit ) ++ "%  "     ++ show g    where s = fitness g mutate :: [Char] -> Int -> IO [Char]mutate g mr = do  let r = length g  chances <- replicateM r \$ randomRIO (1,mr)  let pos = elemIndices 1 chances  chrs <- replicateM (length pos) \$ randomRIO (bounds charSet)  let nchrs = map (charSet!) chrs  return \$ foldl (\ng (p,c) -> replace (p+1) c ng) g (zip pos nchrs) evolve :: [Char] -> Int -> Int -> IO ()evolve parent gen mr = do  when ((gen-1) `mod` 20 == 0) \$ printRes (gen-1) parent  children <- replicateM popsize (mutate parent mr)  let child = maximumBy (comparing fitness) (parent:children)  if fitness child == pfit then printRes gen child                           else evolve child (succ gen) mr main = do  let r = length target  genes <- replicateM r \$ randomRIO (bounds charSet)  let parent = map (charSet!) genes  evolve parent 1 mutateRate`

Example run in GHCi:

```*Main> main
gen:   0  fitn:   4%  "AICJEWXYSFTMOAYOHNFZ HSLFNBY"
gen:  20  fitn:  54%  "XZTHIWXSSVTMSUYOIKEZA WEFSEL"
gen:  40  fitn:  89%  "METHINXSSIT IS OIKE A WEASEL"
gen:  60  fitn:  93%  "METHINXSSIT IS LIKE A WEASEL"
gen:  78  fitn: 100%  "METHINKS IT IS LIKE A WEASEL"```

### Alternate Presentation

I find this easier to read.

`import System.Randomimport Data.Listimport Data.Ordimport Data.Arrayimport Control.Monadimport Control.Arrow target = "METHINKS IT IS LIKE A WEASEL"mutateRate = 0.1popSize = 100printEvery = 10 alphabet = listArray (0,26) (' ':['A'..'Z']) randomChar = (randomRIO (0,26) :: IO Int) >>= return . (alphabet !) origin = mapM createChar target    where createChar c = randomChar fitness = length . filter id . zipWith (==) target mutate = mapM mutateChar    where mutateChar c = do            r <- randomRIO (0.0,1.0) :: IO Double            if r < mutateRate then randomChar else return c converge n parent = do    if n`mod`printEvery == 0 then putStrLn fmtd else return ()    if target == parent        then putStrLn \$ "\nFinal: " ++ fmtd        else mapM mutate (replicate (popSize-1) parent) >>=                converge (n+1) . fst . maximumBy (comparing snd) . map (id &&& fitness) . (parent:)    where fmtd = parent ++ ": " ++ show (fitness parent) ++ " (" ++ show n ++ ")" main = origin >>= converge 0`

Example:

```YUZVNNZ SXPSNGZFRHZKVDOEPIGS: 2 (0)
BEZHANK KIPONSYSPKV F AEULEC: 11 (10)
BETHANKSFIT ISYHIKJ I TERLER: 17 (20)
METHINKS IT IS YIKE R TERYER: 22 (30)
METHINKS IT IS YIKE   WEASEQ: 25 (40)
METHINKS IT IS MIKE   WEASEI: 25 (50)
METHINKS IT IS LIKE D WEASEI: 26 (60)
METHINKS IT IS LIKE T WEASEX: 26 (70)
METHINKS IT IS LIKE I WEASEL: 27 (80)

Final: METHINKS IT IS LIKE A WEASEL: 28 (86)```

## Icon and Unicon

`global target, chars, parent, C, M, current_fitness procedure fitness(s)	fit := 0	#Increment the fitness for every position in the string s that matches the target	every i := 1 to *target & s[i] == target[i] do fit +:= 1	return fitend procedure mutate(s)	#If a random number between 0 and 1 is inside the bounds of mutation randomly alter a character in the string 	if (?0 <= M) then ?s := ?chars	return send procedure generation()	population := [ ]	next_parent := ""	next_fitness := -1 	#Create the next population	every 1 to C do push(population, mutate(parent))	#Find the member of the population with highest fitness, or use the last one inspected	every x := !population & (xf := fitness(x)) > next_fitness do {		next_parent := x		next_fitness := xf	} 	parent := next_parent 	return next_fitnessend procedure main()	target := "METHINKS IT IS LIKE A WEASEL"			#Our target string	chars := &ucase ++ " "						#Set of usable characters	parent := "" & every 1 to *target do parent ||:= ?chars		#The universal common ancestor!	current_fitness := fitness(parent)				#The best fitness we have so far  	C := 50		#Population size in each generation	M := 0.5	#Mutation rate per individual in a generation 	gen := 1	#Until current fitness reaches a score of perfect match with the target string keep generating new populations	until ((current_fitness := generation()) = *target) do {                write(gen || " " || current_fitness || " " || parent)                gen +:= 1	} 		write("At generation " || gen || " we found a string with perfect fitness at " || current_fitness || " reading: " || parent)end `

## J

Solution:
Using sum of differences from the target for fitness, i.e. `0` is optimal fitness.

`CHARSET=: 'ABCDEFGHIJKLMNOPQRSTUVWXYZ 'NPROG=:   100                            NB. number of progeny (C)MRATE=:   0.05                           NB. mutation rate create  =: ([email protected]\$&\$ { ])&CHARSET           NB. creates random list from charset of same shape as yfitness =: +/@:~:"1copy    =: # ,:mutate  =: &(>: \$ [email protected]\$ 0:)(`(,: create))} NB. adverbselect  =: ] {~ (i. <./)@:fitness        NB. select fittest member of population nextgen =: select ] , [: MRATE mutate NPROG copy ]while   =: conjunction def '(] , (u {:))^:(v {:)^:_ ,:' evolve=: nextgen while (0 < fitness) create`

Example usage:
Returns list of best solutions at each generation until converged.

`   filter=: {: ,~ ({~ [email protected]>.&.(%&20)@#)   NB. take every 20th and last item   filter evolve 'METHINKS IT IS LIKE A WEASEL'XXURVQXKQXDLCGFVICCUA NUQPNDMEFHINVQQXT IW LIKEUA WEAPELMETHINVS IT IW LIKEUA WEAPELMETHINKS IT IS LIKE A WEASEL`

Alternative solution:
Using explicit versions of `mutate` and `evolve` above.

`CHARSET=: 'ABCDEFGHIJKLMNOPQRSTUVWXYZ 'NPROG=:   100                             NB. "C" from specification fitness=: +/@:~:"1select=: ] {~ (i. <./)@:fitness           NB. select fittest member of populationpopulate=: ([email protected]\$&# { ])&CHARSET            NB. get random list from charset of same length as ylog=: [: smoutput [: ;:inv (('#';'fitness: ';'; ') ,&.> ":&.>) mutate=: dyad define  idxmut=. I. x >: (*/\$y) [email protected]\$ 0  (populate idxmut) idxmut"_} y) evolve=: monad define  target=. y  parent=. populate y  iter=. 0  mrate=. %#y  while. 0 < val=. target fitness parent do.    if. 0 = 50|iter do. log iter;val;parent end.    iter=. iter + 1    progeny=. mrate mutate NPROG # ,: parent  NB. create progeny by mutating parent copies    parent=. target select parent,progeny     NB. select fittest parent for next generation  end.  log iter;val;parent  parent)`

Example Usage:

`   evolve 'METHINKS IT IS LIKE A WEASEL'#0 fitness: 27 ; YGFDJFTBEDB FAIJJGMFKDPYELOA#50 fitness: 2 ; MEVHINKS IT IS LIKE ADWEASEL#76 fitness: 0 ; METHINKS IT IS LIKE A WEASELMETHINKS IT IS LIKE A WEASEL`

## Java

Works with: Java version 1.5+
(Close)
Translation of: Python
` import java.util.Random; public class EvoAlgo {  static final String target = "METHINKS IT IS LIKE A WEASEL";  static final char[] possibilities = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ".toCharArray();  static int C = 100; //number of spawn per generation  static double minMutateRate = 0.09;  static int perfectFitness = target.length();  private static String parent;  static Random rand = new Random();   private static int fitness(String trial){    int retVal = 0;    for(int i = 0;i < trial.length(); i++){      if (trial.charAt(i) == target.charAt(i)) retVal++;    }    return retVal;  }   private static double newMutateRate(){    return (((double)perfectFitness - fitness(parent)) / perfectFitness * (1 - minMutateRate));  }   private static String mutate(String parent, double rate){    String retVal = "";    for(int i = 0;i < parent.length(); i++){      retVal += (rand.nextDouble() <= rate) ?        possibilities[rand.nextInt(possibilities.length)]:        parent.charAt(i);    }    return retVal;  }   public static void main(String[] args){    parent = mutate(target, 1);    int iter = 0;    while(!target.equals(parent)){      double rate = newMutateRate();      iter++;      if(iter % 100 == 0){        System.out.println(iter +": "+parent+ ", fitness: "+fitness(parent)+", rate: "+rate);      }      String bestSpawn = null;      int bestFit = 0;      for(int i = 0; i < C; i++){        String spawn = mutate(parent, rate);        int fitness = fitness(spawn);        if(fitness > bestFit){          bestSpawn = spawn;          bestFit = fitness;        }      }      parent = bestFit > fitness(parent) ? bestSpawn : parent;    }    System.out.println(parent+", "+iter);  } }`

Output:

```100: MEVHIBXSCG  TP QIK  FZGJ SEL, fitness: 13, rate: 0.4875
200: MEBHINMSVI  IHTQIKW FTDEZSWL, fitness: 15, rate: 0.42250000000000004
300: METHINMSMIA IHUFIKA F WEYSEL, fitness: 19, rate: 0.29250000000000004
400: METHINSS IT IQULIKA F WEGSEL, fitness: 22, rate: 0.195
METHINKS IT IS LIKE A WEASEL, 492```

## JavaScript

Using cross-browser techniques to support Array.reduce and Array.map

`// ------------------------------------- Cross-browser Compatibility ------------------------------------- /* Compatibility code to reduce an array * Source: https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Array/Reduce */if (!Array.prototype.reduce) {    Array.prototype.reduce = function (fun /*, initialValue */ ) {        "use strict";         if (this === void 0 || this === null) throw new TypeError();         var t = Object(this);        var len = t.length >>> 0;        if (typeof fun !== "function") throw new TypeError();         // no value to return if no initial value and an empty array        if (len == 0 && arguments.length == 1) throw new TypeError();         var k = 0;        var accumulator;        if (arguments.length >= 2) {            accumulator = arguments[1];        } else {            do {                if (k in t) {                    accumulator = t[k++];                    break;                }                 // if array contains no values, no initial value to return                if (++k >= len) throw new TypeError();            }            while (true);        }         while (k < len) {            if (k in t) accumulator = fun.call(undefined, accumulator, t[k], k, t);            k++;        }         return accumulator;    };} /* Compatibility code to map an array * Source: https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Array/Map */if (!Array.prototype.map) {    Array.prototype.map = function (fun /*, thisp */ ) {        "use strict";         if (this === void 0 || this === null) throw new TypeError();         var t = Object(this);        var len = t.length >>> 0;        if (typeof fun !== "function") throw new TypeError();         var res = new Array(len);        var thisp = arguments[1];        for (var i = 0; i < len; i++) {            if (i in t) res[i] = fun.call(thisp, t[i], i, t);        }         return res;    };} /* ------------------------------------- Generator ------------------------------------- * Generates a fixed length gene sequence via a gene strategy object. * The gene strategy object must have two functions: *	- "create": returns create a new gene  *	- "mutate(existingGene)": returns mutation of an existing gene   */function Generator(length, mutationRate, geneStrategy) {    this.size = length;    this.mutationRate = mutationRate;    this.geneStrategy = geneStrategy;} Generator.prototype.spawn = function () {    var genes = [],        x;    for (x = 0; x < this.size; x += 1) {        genes.push(this.geneStrategy.create());    }    return genes;}; Generator.prototype.mutate = function (parent) {    return parent.map(function (char) {        if (Math.random() > this.mutationRate) {            return char;        }        return this.geneStrategy.mutate(char);    }, this);}; /* ------------------------------------- Population ------------------------------------- * Helper class that holds and spawns a new population. */function Population(size, generator) {    this.size = size;    this.generator = generator;     this.population = [];    // Build initial popuation;    for (var x = 0; x < this.size; x += 1) {        this.population.push(this.generator.spawn());    }} Population.prototype.spawn = function (parent) {    this.population = [];    for (var x = 0; x < this.size; x += 1) {        this.population.push(this.generator.mutate(parent));    }}; /* ------------------------------------- Evolver ------------------------------------- * Attempts to converge a population based a fitness strategy object. * The fitness strategy object must have three function   *	- "score(individual)": returns a score for an individual. *	- "compare(scoreA, scoreB)": return true if scoreA is better (ie more fit) then scoreB *	- "done( score )": return true if score is acceptable (ie we have successfully converged).  */function Evolver(size, generator, fitness) {    this.done = false;    this.fitness = fitness;    this.population = new Population(size, generator);} Evolver.prototype.getFittest = function () {    return this.population.population.reduce(function (best, individual) {        var currentScore = this.fitness.score(individual);        if (best === null || this.fitness.compare(currentScore, best.score)) {            return {                score: currentScore,                individual: individual            };        } else {            return best;        }    }, null);}; Evolver.prototype.doGeneration = function () {    this.fittest = this.getFittest();    this.done = this.fitness.done(this.fittest.score);    if (!this.done) {        this.population.spawn(this.fittest.individual);    }}; Evolver.prototype.run = function (onCheckpoint, checkPointFrequency) {    checkPointFrequency = checkPointFrequency || 10; // Default to Checkpoints every 10 generations    var generation = 0;    while (!this.done) {        this.doGeneration();        if (generation % checkPointFrequency === 0) {            onCheckpoint(generation, this.fittest);        }        generation += 1;    }    onCheckpoint(generation, this.fittest);    return this.fittest;}; // ------------------------------------- Exports -------------------------------------window.Generator = Generator;window.Evolver = Evolver;  // helper utitlity to combine elements of two arrays.Array.prototype.zip = function (b, func) {    var result = [],        max = Math.max(this.length, b.length),        x;    for (x = 0; x < max; x += 1) {        result.push(func(this[x], b[x]));    }    return result;}; var target = "METHINKS IT IS LIKE A WEASEL", geneStrategy, fitness, target, generator, evolver, result; geneStrategy = {    // The allowed character set (as an array)     characterSet: "ABCDEFGHIJKLMNOPQRSTUVWXYZ ".split(""),     /*        Pick a random character from the characterSet    */    create: function getRandomGene() {        var randomNumber = Math.floor(Math.random() * this.characterSet.length);        return this.characterSet[randomNumber];    }};geneStrategy.mutate = geneStrategy.create; // Our mutation stragtegy is to simply get a random genefitness = {    // The target (as an array of characters)    target: target.split(""),    equal: function (geneA, geneB) {        return (geneA === geneB ? 0 : 1);    },    sum: function (runningTotal, value) {        return runningTotal + value;    },     /*        We give one point to for each corect letter    */    score: function (genes) {        var diff = genes.zip(this.target, this.equal); // create an array of ones and zeros         return diff.reduce(this.sum, 0); // Sum the array values together.    },    compare: function (scoreA, scoreB) {        return scoreA <= scoreB; // Lower scores are better    },    done: function (score) {        return score === 0; // We have matched the target string.    }}; generator = new Generator(target.length, 0.05, geneStrategy);evolver = new Evolver(100, generator, fitness); function showProgress(generation, fittest) {    document.write("Generation: " + generation + ", Best: " + fittest.individual.join("") + ", fitness:" + fittest.score + "<br>");}result = evolver.run(showProgress);`

Output:

```Generation: 0, Best: KSTFOKJC XZYLWCLLGYZJNXYEGHE, fitness:25
Generation: 10, Best: KOTFINJC XX LS LIGYZT WEPSHL, fitness:14
Generation: 20, Best: KBTHINKS BT LS LIGNZA WEPSEL, fitness:8
Generation: 30, Best: KETHINKS IT BS LISNZA WEASEL, fitness:5
Generation: 40, Best: KETHINKS IT IS LIKEZA WEASEL, fitness:2
Generation: 50, Best: METHINKS IT IS LIKEZA WEASEL, fitness:1
Generation: 52, Best: METHINKS IT IS LIKE A WEASEL, fitness:0
```

## Julia

Works with: Julia version 0.6
`fitness(a::AbstractString, b::AbstractString) = count(l == t for (l, t) in zip(a, b))function mutate(str::AbstractString, rate::Float64)    L = collect(Char, " ABCDEFGHIJKLMNOPQRSTUVWXYZ")    return map(str) do c        if rand() < rate rand(L) else c end    endend function evolve(parent::String, target::String, mutrate::Float64, nchild::Int)    println("Initial parent is \$parent, its fitness is \$(fitness(parent, target))")    gens = 0    while parent != target        children = collect(mutate(parent, mutrate) for i in 1:nchild)        bestfit, best = findmax(fitness.(children, target))        parent = children[best]        gens += 1        if gens % 10 == 0            println("After \$gens generations, the new parent is \$parent and its fitness is \$(fitness(parent, target))")        end    end    println("After \$gens generations, the parent evolved into the target \$target")end evolve("IU RFSGJABGOLYWF XSMFXNIABKT", "METHINKS IT IS LIKE A WEASEL", 0.08998, 100)`
Output:
```Initial parent is IU RFSGJABGOLYWF XSMFXNIABKT, its fitness is 1
After 10 generations, the new parent is MOTBSNGTABTTIL LIXEMA WMALSN and its fitness is 13
After 20 generations, the new parent is METHINGATITUIS LIXE A WEASEQ and its fitness is 22
After 30 generations, the new parent is METHINKSLIT ISELIAE A WEASES and its fitness is 24
After 40 generations, the new parent is METHINKS IT IS LINE A WEASEL and its fitness is 27
After 50 generations, the new parent is METHINKS IT IS LINE A WEASEL and its fitness is 27
After 60 generations, the new parent is METHINKS IT IS PIKE A WEASEL and its fitness is 27
After 70 generations, the new parent is METHINKS IT IS AIKE A WEASEL and its fitness is 27
After 80 generations, the new parent is METHINKS IT IS AIKE A WEASEL and its fitness is 27
After 81 generations, the parent evolved into the target METHINKS IT IS LIKE A WEASEL```

## Kotlin

`import java.util.* val target = "METHINKS IT IS LIKE A WEASEL"val validChars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ " val random = Random() fun randomChar() = validChars[random.nextInt(validChars.length)]fun hammingDistance(s1: String, s2: String) =        s1.zip(s2).map { if (it.first == it.second) 0 else 1 }.sum() fun fitness(s1: String) = target.length - hammingDistance(s1, target) fun mutate(s1: String, mutationRate: Double) =        s1.map { if (random.nextDouble() > mutationRate) it else randomChar() }                .joinToString(separator = "") fun main(args: Array<String>) {    val initialString = (0 until target.length).map { randomChar() }.joinToString(separator = "")     println(initialString)    println(mutate(initialString, 0.2))     val mutationRate = 0.05    val childrenPerGen = 50     var i = 0    var currVal = initialString    while (currVal != target) {        i += 1        currVal = (0..childrenPerGen).map { mutate(currVal, mutationRate) }.maxBy { fitness(it) }!!    }    println("Evolution found target after \$i generations")}`

## Liberty BASIC

`C = 10'mutaterate has to be greater than 1 or it will not mutatemutaterate = 2mutationstaken = 0generations = 0Dim parentcopies\$((C - 1))Global targetString\$ : targetString\$ = "METHINKS IT IS LIKE A WEASEL"Global allowableCharacters\$ : allowableCharacters\$ = " ABCDEFGHIJKLMNOPQRSTUVWXYZ"currentminFitness = Len(targetString\$) For i = 1 To Len(targetString\$)    parent\$ = parent\$ + Mid\$(allowableCharacters\$, Int(Rnd(1) * Len(allowableCharacters\$)), 1)Next i Print "Parent = " + parent\$ While parent\$ <> targetString\$    generations = (generations + 1)    For i = 0 To (C - 1)        parentcopies\$(i) = mutate\$(parent\$, mutaterate)        mutationstaken = (mutationstaken + 1)    Next i    For i = 0 To (C - 1)        currentFitness = Fitness(targetString\$, parentcopies\$(i))        If currentFitness = 0 Then            parent\$ = parentcopies\$(i)            Exit For        Else            If currentFitness < currentminFitness Then                currentminFitness = currentFitness                parent\$ = parentcopies\$(i)            End If        End If    Next i    CLS    Print "Generation - " + str\$(generations)    Print "Parent - " + parent\$    ScanWend PrintPrint "Congratulations to me; I finished!"Print "Final Mutation: " + parent\$'The ((i + 1) - (C)) reduces the total number of mutations that it took by one generation'minus the perfect child mutation since any after that would not have been required.Print "Total Mutations Taken - " + str\$(mutationstaken - ((i + 1) - (C)))Print "Total Generations Taken - " + str\$(generations)Print "Child Number " + str\$(i) + " has perfect similarities to your target."End   Function mutate\$(mutate\$, mutaterate)        If (Rnd(1) * mutaterate) > 1 Then            'The mutatingcharater randomizer needs 1 more than the length of the string            'otherwise it will likely take forever to get exactly that as a random number            mutatingcharacter = Int(Rnd(1) * (Len(targetString\$) + 1))            mutate\$ = Left\$(mutate\$, (mutatingcharacter - 1))  + Mid\$(allowableCharacters\$, Int(Rnd(1) * Len(allowableCharacters\$)), 1) _                      + Mid\$(mutate\$, (mutatingcharacter + 1))        End IfEnd Function Function Fitness(parent\$, offspring\$)    For i = 1 To Len(targetString\$)        If Mid\$(parent\$, i, 1) <> Mid\$(offspring\$, i, 1) Then            Fitness = (Fitness + 1)        End If    Next iEnd Function`

## Logo

`make "target "|METHINKS IT IS LIKE A WEASEL| to distance :w  output reduce "sum (map.se [ifelse equal? ?1 ?2 [0][1]] :w :target)end to random.letter  output pick "| ABCDEFGHIJKLMNOPQRSTUVWXYZ|end to mutate :parent :rate  output map [ifelse random 100 < :rate [random.letter] [?]] :parentend make "C 100make "mutate.rate 10     ; percent to breed :parent  make "parent.distance distance :parent  localmake "best.child :parent  repeat :C [    localmake "child mutate :parent :mutate.rate    localmake "child.distance distance :child    if greater? :parent.distance :child.distance [      make "parent.distance :child.distance      make "best.child :child    ]  ]  output :best.childend to progress  output (sentence :trials :parent "distance: :parent.distance)end to evolve  make "parent cascade count :target [lput random.letter ?] "||  make "trials 0  while [not equal? :parent :target] [    make "parent breed :parent    print progress    make "trials :trials + 1  ]end`

## Lua

Works with: Lua version 5.1+
`local target = "METHINKS IT IS LIKE A WEASEL"local alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "local c, p = 100, 0.06 local function fitness(s)	local score = #target	for i = 1,#target do		if s:sub(i,i) == target:sub(i,i) then score = score - 1 end	end	return scoreend local function mutate(s, rate)	local result, idx = ""	for i = 1,#s do		if math.random() < rate then			idx = math.random(#alphabet)			result = result .. alphabet:sub(idx,idx)		else			result = result .. s:sub(i,i)		end	end	return result, fitness(result)end local function randomString(len)	local result, idx = ""	for i = 1,len do		idx = math.random(#alphabet)		result = result .. alphabet:sub(idx,idx)	end	return resultend local function printStep(step, s, fit)	print(string.format("%04d: ", step) .. s .. " [" .. fit .."]")end math.randomseed(os.time())local parent = randomString(#target)printStep(0, parent, fitness(parent)) local step = 0while parent ~= target do	local bestFitness, bestChild, child, fitness = #target + 1	for i = 1,c do		child, fitness = mutate(parent, p)		if fitness < bestFitness then bestFitness, bestChild = fitness, child end	end	parent, step = bestChild, step + 1	printStep(step, parent, bestFitness)end`

## M2000 Interpreter

### Version 1

` Module WeaselAlgorithm {      Print "Evolutionary Algorithm"      \\ Weasel Algorithm      \\ Using dynamic array, which expand if no fitness change,       \\ and reduce to minimum when fitness changed      \\ Abandon strings when fitness change      \\ Also lambda function Mutate\$ change when topscore=10, to change only one character      l\$="ABCDEFGHIJKLMNOPQRSTUVWXYZ "      randomstring\$=lambda\$ l\$ ->{            res\$=""            For i=1 to 28: res\$+=Mid\$(L\$,Random(1,27),1):next i            =res\$      }      m\$="METHINKS IT IS LIKE A WEASEL"      lm=len(m\$)      fitness=lambda m\$, lm  (this\$)-> {            score=0 : For i=1 to lm {score+=If(mid\$(m\$,i,1)=mid\$(this\$, i, 1)->1,0)} : =score      }      Mutate\$=lambda\$ l\$ (w\$)-> {            a=random(1,28) : insert a, 1 w\$=mid\$(l\$, random(1,27),1)            If random(3)=1 Then b=a:while b=a {b=random(1,28)} : insert b, 1 w\$=mid\$(l\$, random(1,27),1)            =w\$      }      Mutate1\$=lambda\$ l\$ (w\$)-> {            insert random(1,28), 1 w\$=mid\$(l\$, random(1,27),1) : =w\$      }      f\$=randomstring\$()      topscore=0      last=0      Pen 11 {Print "Fitness |Target:", @(16),m\$, @(47),"|Total Strings"}      Print Over \$(3,8), str\$(topscore/28,"##0.0%"),"",\$(0),f\$, 0      count=0      gen=30      mut=0      {            last=0            Dim a\$(1 to gen)<<mutate\$(f\$)            mut+=gen            oldscore=topscore            For i=1 to gen {                  topscore=max.data(topscore, fitness(a\$(i)))                  If oldscore<topscore Then last=i:Exit            }            If last>0 Then {                  f\$=a\$(last) : gen=30 : If topscore=10 Then mutate\$=mutate1\$            } Else gen+=50            Print Over \$(3,8), str\$(topscore/28,"##0.0%"), "",\$(0),f\$, mut : refresh            count+=min(gen,i)            If topscore<28 Then loop      }       Print       Print "Results"      Print "I found this:"; a\$(i)      Print "Total strings which evalute fitness:"; count      Print "Done"}WeaselAlgorithm  `
Output:
```Fitness |Target: METHINKS IT IS LIKE A WEASEL |Total strings
3,6%         ZZBZSVEOWPSQGJXNIXTFQCDQTJFE        30
7,1%         ZZBZSVEOWPSQGJXNIXTFQCDQAJFE        60
14,3%         ZZBZSVEOWPTQGJXNIXTFACDQAJFE        90
17,9%         ZZBZSVEOWPTQGJXNIXTFA DQAJFE       200
21,4%         ZEBZSVEOWPTQGJXNIXTFA DQAJFE       230
25,0%         ZEBZSVEOWPTQGJXNIXT A DQAJFE       260
28,6%         MEBZSVEOCPTQGJXNIXT A DQAJFE       290
32,1%         MEBZSVEOCITQGJXNIXT A DQAJFE       320
35,7%         MEBZSVEOCITQGJXNIKT A DQAJFE       350
39,3%         MEBZSVEOCITQGJ NIKT A DQAJFE       380
42,9%         MEBZSVEOCITQGJ NIKT A WQAJFE       410
46,4%         MEBZSVESCITQGJ NIKT A WQAJFE       440
50,0%         MEBZSVESCITQIJ NIKT A WQAJFE       680
53,6%         MEBZSVESCIT IJ NIKT A WQAJFE      1100
57,1%         MEBZSVESCIT IJ LIKT A WQAJFE      1130
60,7%         MEBZSVKSCIT IJ LIKT A WQAJFE      1240
64,3%         MEBZSVKS IT IJ LIKT A WQAJFE      1480
67,9%         MEBZSNKS IT IJ LIKT A WQAJFE      1900
71,4%         MEBHSNKS IT IJ LIKT A WQAJFE      2010
75,0%         METHSNKS IT IJ LIKT A WQAJFE      2430
78,6%         METHSNKS IT IJ LIKE A WQAJFE      2670
82,1%         METHSNKS IT IJ LIKE A WQAJFL      3090
85,7%         METHSNKS IT IJ LIKE A WEAJFL      3330
89,3%         METHSNKS IT IJ LIKE A WEASFL      3980
92,9%         METHINKS IT IJ LIKE A WEASFL      4400
96,4%         METHINKS IT IJ LIKE A WEASEL      5050
100,0%         METHINKS IT IS LIKE A WEASEL      5290
Results
I found this:METHINKS IT IS LIKE A WEASEL
Total strings which evaluate fitness:3230
```

### Version 2

The second version check fitness for all strings until became 28 (100%)

Also here we have one Mutate function which change letters using 5% probability for each place in the parent string.

` Module WeaselAlgorithm2 {      Print "Evolutionary Algorithm"      \\ Weasel Algorithm      \\ Using dynamic array, which expand if no fitness change,       \\ and reduce to minimum when fitness changed      l\$="ABCDEFGHIJKLMNOPQRSTUVWXYZ "      randomstring\$=lambda\$ l\$ ->{            res\$=""            For i=1 to 28: res\$+=Mid\$(L\$,Random(1,27),1):next i            =res\$      }      m\$="METHINKS IT IS LIKE A WEASEL"      lm=len(m\$)      fitness=lambda m\$, lm  (this\$)-> {            score=0 : For i=1 to lm {score+=If(mid\$(m\$,i,1)=mid\$(this\$, i, 1)->1,0)} : =score      }      Mutate\$=lambda\$ l\$ (w\$)-> {            for i=1 to len(w\$) {                  if random(1,100)<=5 then { insert i, 1 w\$=mid\$(l\$, random(1,27),1)  }            }            =w\$      }      f\$=randomstring\$()      topscore=0      last=0      Pen 11 {Print "Fitness |Target:", @(16),m\$, @(47),"|Total Strings"}      Print Over \$(3,8), str\$(topscore/28,"##0.0%"),"",\$(0),f\$, 0      count=0      gen=30      mut=0      {            last=0            Dim a\$(1 to gen)<<mutate\$(f\$)            mut+=gen            oldscore=topscore            For i=1 to gen {                  topscore=max.data(topscore, fitness(a\$(i)))                  If oldscore<topscore Then last=i: oldscore=topscore            }            If last>0 Then {                  f\$=a\$(last) : gen=30             } Else gen+=50            Print Over \$(3,8), str\$(topscore/28,"##0.0%"), "",\$(0),f\$, mut : refresh            count+=min(gen,i)            If topscore<28 Then loop      }       Print       Print "Results"      Print "I found this:"; a\$(last)      Print "Total strings which evalute fitness:"; count      Print "Done"}WeaselAlgorithm2 `

## Mathematica / Wolfram Language

`target = "METHINKS IT IS LIKE A WEASEL";alphabet = CharacterRange["A", "Z"]~Join~{" "};fitness = HammingDistance[target, #] &;Mutate[parent_String, rate_: 0.01, fertility_Integer: 25] := Module[   {offspring, kidfits, gen = 0, alphabet = CharacterRange["A", "Z"]~Join~{" "}},   offspring = ConstantArray[Characters[parent], fertility];   Table[    If[RandomReal[] <= rate, offspring[[j, k]] = RandomChoice[alphabet]],    {j, fertility}, {k, [email protected]}    ];   offspring = StringJoin[#] & /@ offspring;   kidfits = fitness[#] & /@ Flatten[{offspring, parent}];   Return[offspring[[[email protected][kidfits]]]];   ]; mutationRate = 0.02;parent = StringJoin[ alphabet[[RandomInteger[{1, [email protected]}, [email protected]]]] ];results = NestWhileList[Mutate[#, mutationRate, 100] &, parent, fitness[#] > 0 &];fits = fitness[#] & /@ results;results = Transpose[{results, fits}];TableForm[results[[;; ;; 2]], TableHeadings->{Range[1, [email protected], 2],{"String","Fitness"}}, TableSpacing -> {1, 2}] `

Output:

```GBPQVCRDTMCPVZBRLLRKPF GXATW	28
GBTQVCKDTMTPVZBRLLEKPF GXATW	24
GBTQICKDTMTPVZBILLE PF GXATL	21
GBTQICKD ITPVZBILLE PF EXATL	18
GBTQICKD ITPVZBPILE PS EAAVL	16
GBTQICKS ITPVZBLILE A WEAAVL	11
GBTQICKS ITPVSBLILE A WEAAEL	9
METQICKS ITPVS LIHE A WEAAEL	6
METHICKS ITPIS LIKE A WEAAEL	3
METHINKS ITPIS LIKE A WEAYEL	2
METHINKS IT IS LIKE A WEAYEL	1
METHINKS IT IS LIKE A WEAYEL	1
METHINKS IT IS LIKE A WEATEL	1
METHINKS IT IS LIKE A WEATEL	1
METHINKS IT IS LIKE A WEATEL	1
METHINKS IT IS LIKE A WEAXEL	1
METHINKS IT IS LIKE A WEASEL	0```

## MATLAB

This solution implements a class called EvolutionaryAlgorithm, the members of the class are the variables required by the task description. You can see them using the disp() function on an instance of the class. To use this class you only need to specify the target, mutation rate, number of children (called C in the task spec), and maximum number of evolutionary cycles. After doing so, call the evolve() function on the class instance to start the evolution cycle. Note, the fitness function computes the hamming distance between the target string and another string, this can be changed if a better heuristic exists.

To use this code, create a folder in your MATLAB directory titled "@EvolutionaryAlgorithm". Within that folder save this code in a file named "EvolutionaryAlgorithm.m".

`%This class impliments a string that mutates to a targetclassdef EvolutionaryAlgorithm     properties         target;        parent;        children = {};        validAlphabet;         %Constants        numChildrenPerIteration;        maxIterations;        mutationRate;     end     methods         %Class constructor        function family = EvolutionaryAlgorithm(target,mutationRate,numChildren,maxIterations)             family.validAlphabet = char([32 (65:90)]); %Space char and A-Z            family.target = target;            family.children = cell(numChildren,1);            family.numChildrenPerIteration = numChildren;            family.maxIterations = maxIterations;            family.mutationRate = mutationRate;            initialize(family);         end %class constructor         %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%        %Helper functions and class get/set functions        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%         %setAlphabet() - sets the valid alphabet for the current instance        %of the EvolutionaryAlgorithm class.        function setAlphabet(family,alphabet)             if(ischar(alphabet))                family.validAlphabet = alphabet;                 %Makes change permanent                assignin('caller',inputname(1),family);             else                error 'New alphabet must be a string or character array';            end                     end         %setTarget() - sets the target for the current instance        %of the EvolutionaryAlgorithm class.        function setTarget(family,target)             if(ischar(target))                family.target = target;                 %Makes change permanent                assignin('caller',inputname(1),family);             else                error 'New target must be a string or character array';            end                     end         %setMutationRate() - sets the mutation rate for the current instance        %of the EvolutionaryAlgorithm class.        function setMutationRate(family,mutationRate)             if(isnumeric(mutationRate))                family.mutationRate = mutationRate;                 %Makes change permanent                assignin('caller',inputname(1),family);             else                error 'New mutation rate must be a double precision number';            end                     end         %setMaxIterations() - sets the maximum number of iterations during        %evolution for the current instance of the EvolutionaryAlgorithm class.        function setMaxIterations(family,maxIterations)             if(isnumeric(maxIterations))                family.maxIterations = maxIterations;                 %Makes change permanent                assignin('caller',inputname(1),family);             else                error 'New maximum amount of iterations must be a double precision number';            end                     end         %display() - overrides the built-in MATLAB display() function, to        %display the important class variables        function display(family)            disp([sprintf('Target: %s\n',family.target)...                  sprintf('Parent: %s\n',family.parent)...                  sprintf('Valid Alphabet: %s\n',family.validAlphabet)...                  sprintf('Number of Children: %d\n',family.numChildrenPerIteration)...                  sprintf('Mutation Rate [0,1]: %d\n',family.mutationRate)...                  sprintf('Maximum Iterations: %d\n',family.maxIterations)]);                         end         %disp() - overrides the built-in MATLAB disp() function, to        %display the important class variables        function disp(family)            display(family);        end         %randAlphabetElement() - Generates a random character from the        %valid alphabet for the current instance of the class.        function elements = randAlphabetElements(family,numChars)             %Sample the valid alphabet randomly from the uniform            %distribution            N = length(family.validAlphabet);            choices = ceil(N*rand(1,numChars));             elements = family.validAlphabet(choices);         end         %initialize() - Sets the parent to a random string of length equal        %to the length of the target        function parent = initialize(family)             family.parent = randAlphabetElements(family,length(family.target));            parent = family.parent;             %Makes changes to the instance of EvolutionaryAlgorithm permanent            assignin('caller',inputname(1),family);          end %initialize         %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%        %Functions required by task specification        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%         %mutate() - generates children from the parent and mutates them        function mutate(family)             sizeParent = length(family.parent);             %Generate mutatant children sequentially            for child = (1:family.numChildrenPerIteration)                 parentCopy = family.parent;                 for charIndex = (1:sizeParent)                     if (rand(1) < family.mutationRate)                        parentCopy(charIndex) = randAlphabetElements(family,1);                    end                end                 family.children{child} = parentCopy;             end             %Makes changes to the instance of EvolutionaryAlgorithm permanent            assignin('caller',inputname(1),family);           end %mutate         %fitness() - Computes the Hamming distance between the target        %string and the string input as the familyMember argument        function theFitness = fitness(family,familyMember)             if not(ischar(familyMember))                error 'The second argument must be a string';            end             theFitness = sum(family.target == familyMember);        end         %evolve() - evolves the family until the target is reached or it         %exceeds the maximum amount of iterations                function [iteration,mostFitFitness] = evolve(family)             iteration = 0;            mostFitFitness = 0;            targetFitness = fitness(family,family.target);             disp(['Target fitness is ' num2str(targetFitness)]);             while (mostFitFitness < targetFitness) && (iteration < family.maxIterations)                 iteration = iteration + 1;                 mutate(family);                 parentFitness = fitness(family,family.parent);                                mostFit = family.parent;                mostFitFitness = parentFitness;                 for child = (1:family.numChildrenPerIteration)                     childFitness = fitness(family,family.children{child});                    if childFitness > mostFitFitness                        mostFit = family.children{child};                        mostFitFitness = childFitness;                    end                 end                                family.parent = mostFit;                disp([num2str(iteration) ': ' mostFit ' - Fitness: ' num2str(mostFitFitness)]);             end             %Makes changes to the instance of EvolutionaryAlgorithm permanent            assignin('caller',inputname(1),family);         end %evolve     end %methodsend %classdef`

Sample Output: (Some evolutionary cycles omitted for brevity)

`>> instance = EvolutionaryAlgorithm('METHINKS IT IS LIKE A WEASEL',.08,50,1000)Target: METHINKS IT IS LIKE A WEASELParent: UVEOCXXFBGDCSFNMJQNWTPJ PCVAValid Alphabet:  ABCDEFGHIJKLMNOPQRSTUVWXYZNumber of Children: 50Mutation Rate [0,1]: 8.000000e-002Maximum Iterations: 1000 >> evolve(instance);Target fitness is 281: MVEOCXXFBYD SFCMJQNWTPM PCVA - Fitness: 22: MEEOCXXFBYD SFCMJQNWTPM PCVA - Fitness: 33: MEEHCXXFBYD SFCMJXNWTPM ECVA - Fitness: 44: MEEHCXXFBYD SFCMJXNWTPM ECVA - Fitness: 45: METHCXAFBYD SFCMJXNWXPMARPVA - Fitness: 56: METHCXAFBYDFSFCMJXNWX MARSVA - Fitness: 67: METHCXKFBYDFBFCQJXNWX MATSVA - Fitness: 78: METHCXKFBYDFBF QJXNWX MATSVA - Fitness: 89: METHCXKFBYDFBF QJXNWX MATSVA - Fitness: 810: METHCXKFUYDFBF QJXNWX MITSEA - Fitness: 920: METHIXKF YTBOF LIKN G MIOSEI - Fitness: 1630: METHIXKS YTCOF LIKN A MIOSEL - Fitness: 1940: METHIXKS YTCIF LIKN A MEUSEL - Fitness: 2150: METHIXKS YT IS LIKE A PEUSEL - Fitness: 24100: METHIXKS YT IS LIKE A WEASEL - Fitness: 26150: METHINKS YT IS LIKE A WEASEL - Fitness: 27195: METHINKS IT IS LIKE A WEASEL - Fitness: 28`

### Genetic Algorithm Example

This solution uses a subset of evolutionary programming called the Genetic Algorithm. It is very similar to the basic evolutionary algorithm, but instead of just using mutations it also makes use of other genetic operators. The algorithm begins by importing the target text (in this case 'METHINKS IT IS LIKE A WEASEL') and then the algorithm performs genetic operations until the target string is obtained or the maximum number of iterations is reached (which will never happen with the given target string). The algorithm first measures how fit each potential answer is, and then selects strings to perform operations on. The selected answers go through the crossover stage where their data is split and recombined into new potential answers. Then a chance for the answer to mutate slightly occurs and the algorithm repeats itself.

Presented is very efficient and vectorized version of the genetic algorithm. To run the algorithm simply copy and paste the code into a script and hit run. You can adjust the style of selection and crossover used to learn more about how they effect solutions. The algorithm can also handle any target string that uses ASCII characters and will allow for any phrase to be used regardless of length.

` %% Genetic Algorithm -- Solves For A User Input String % #### PLEASE NOTE: you can change the selection and crossover type in the% parameters and see how the algorithm changes. #### clear;close all;clc;    %Clears variables, closes windows, and clears the command windowtic                     % Begins the timer %% Select Target Stringtarget  = 'METHINKS IT IS LIKE A WEASEL';% *Can Be Any String With Any Values and Any Length!*% but for this example we use 'METHINKS IT IS LIKE A WEASEL' %% Parameters                    popSize = 1000;                                 % Population Size (100-10000 generally produce good results)genome  = length(target);                       % Genome SizemutRate = .01;                                  % Mutation Rate (5%-25% produce good results)S       = 4;                                    % Tournament Size (2-6 produce good results)best    = Inf;                                  % Initialize Best (arbitrarily large)MaxVal  = max(double(target));                  % Max Integer Value Neededideal   = double(target);                       % Convert Target to Integers selection = 0;                                  % 0: Tournament                                                % 1: 50% Truncation crossover = 1;                                  % 0: Uniform crossover                                                % 1: 1 point crossover                                                % 2: 2 point crossover%% Initialize PopulationPop = round(rand(popSize,genome)*(MaxVal-1)+1); % Creates Population With Corrected Genome Length for Gen = 1:1e6                                 % A Very Large Number Was Chosen, But Shouldn't Be Needed     %% Fitness     % The fitness function starts by converting the characters into integers and then    % subtracting each element of each member of the population from each element of     % the target string. The function then takes the absolute value of     % the differences and sums each row and stores the function as a mx1 matrix.     F = sum(abs(bsxfun(@minus,Pop,ideal)),2);              % Finding Best Members for Score Keeping and Printing Reasons    [current,currentGenome] = min(F);   % current is the minimum value of the fitness array F                                        % currentGenome is the index of that value in the F array     % Stores New Best Values and Prints New Best Scores    if current < best        best = current;        bestGenome = Pop(currentGenome,:); % Uses that index to find best value         fprintf('Gen: %d  |  Fitness: %d  |  ',Gen, best);  % Formatted printing of generation and fitness        disp(char(bestGenome));                             % Best genome so far    elseif best == 0        break                                               % Stops program when we are done    end     %% Selection     % TOURNAMENT    if selection == 0    T = round(rand(2*popSize,S)*(popSize-1)+1);                     % Tournaments    [~,idx] = min(F(T),[],2);                                       % Index to Determine Winners             W = T(sub2ind(size(T),(1:2*popSize)',idx));                     % Winners     % 50% TRUNCATION    elseif selection == 1    [~,V] = sort(F,'descend');                                      % Sort Fitness in Ascending Order    V = V(popSize/2+1:end);                                         % Winner Pool    W = V(round(rand(2*popSize,1)*(popSize/2-1)+1))';               % Winners        end     %% Crossover     % UNIFORM CROSSOVER    if crossover == 0    idx = logical(round(rand(size(Pop))));                          % Index of Genome from Winner 2    Pop2 = Pop(W(1:2:end),:);                                       % Set Pop2 = Pop Winners 1    P2A = Pop(W(2:2:end),:);                                        % Assemble Pop2 Winners 2    Pop2(idx) = P2A(idx);                                           % Combine Winners 1 and 2     % 1-POINT CROSSOVER    elseif crossover == 1    Pop2 = Pop(W(1:2:end),:);                                       % New Population From Pop 1 Winners    P2A = Pop(W(2:2:end),:);                                        % Assemble the New Population    Ref = ones(popSize,1)*(1:genome);                               % The Reference Matrix    idx = (round(rand(popSize,1)*(genome-1)+1)*ones(1,genome))>Ref; % Logical Indexing    Pop2(idx) = P2A(idx);                                           % Recombine Both Parts of Winners     % 2-POINT CROSSOVER    elseif crossover == 2    Pop2 = Pop(W(1:2:end),:);                                       % New Pop is Winners of old Pop    P2A  = Pop(W(2:2:end),:);                                       % Assemble Pop2 Winners 2    Ref  = ones(popSize,1)*(1:genome);                              % Ones Matrix    CP   = sort(round(rand(popSize,2)*(genome-1)+1),2);             % Crossover Points    idx = CP(:,1)*ones(1,genome)<Ref&CP(:,2)*ones(1,genome)>Ref;    % Index    Pop2(idx)=P2A(idx);                                             % Recombine Winners    end    %% Mutation     idx = rand(size(Pop2))<mutRate;                                 % Index of Mutations    Pop2(idx) = round(rand([1,sum(sum(idx))])*(MaxVal-1)+1);        % Mutated Value     %% Reset Poplulations    Pop = Pop2;                                                    end toc % Ends timer and prints elapsed time `

Sample Output: (The Algorithm was run with 1000 population members, Tournament Selection (with tournament size of 4), 1-Point Crossover, and a mutation rate of 10%).

` Gen: 1  |  Fitness: 465  |  C�I1%G+<%?R�8>9�JU#(E�UO�PHIGen: 2  |  Fitness: 429  |  W=P6>D�I)VU6\$T 99,� B�BMP0JHGen: 3  |  Fitness: 366  |  P�;R08AS�GJ�IS&T38IE�)SJERLJGen: 4  |  Fitness: 322  |  KI8M5LAS�GJ�IS�SP�@)D�[email protected]JCPGen: 5  |  Fitness: 295  |  UAUR08AS�GJ�IS�8HG*�+�=C?UB(Gen: 6  |  Fitness: 259  |  VCUQH35S�HR4.L�ISJQ%J�OC*T=EGen: 7  |  Fitness: 226  |  LFB8GPET(LODKQ�KQ<K	E*PEMA6IGen: 8  |  Fitness: 192  |  EPKOLCIR�QQ�NF�QG:B(D/U>BQGFGen: 9  |  Fitness: 159  |  N8R7?SOU�NO\$OK O?K?!;�MB?QHGGen: 10  |  Fitness: 146  |  [email protected])PS%IS#TFJQ%A!U>BVLIGen: 11  |  Fitness: 120  |  L?VMALJS%?R EK IILE�6'RRERLJGen: 12  |  Fitness: 102  |  [email protected]�NU CS*R?K?!; VD>LCLGen: 13  |  Fitness: 96  |  NENMVOMR�NU CS*R?K?!; VD>LCLGen: 14  |  Fitness: 82  |  REJGNPMU�KR CS [email protected]+D�UD?QHGGen: 15  |  Fitness: 75  |  NETI=HPQ�FT ID EFKE D"WD>QDQGen: 16  |  Fitness: 70  |  [email protected])@R\$IS KKLE�D"WC?UBJGen: 17  |  Fitness: 61  |  NESIKQRP�NU CS�MFKE ; SEETCPGen: 18  |  Fitness: 57  |  LFSGLPTN�NU GQ IIKE D"VD>LCLGen: 19  |  Fitness: 40  |  NENKJLMS�GS%IS#MFKE B UFATCLGen: 21  |  Fitness: 39  |  NETIGPEU�KR IS IIKD"? UFDQEKGen: 22  |  Fitness: 33  |  NETGCOMT�LU IS#MFKE B UFATCLGen: 23  |  Fitness: 32  |  NETIKNPQ�NU IS#IIKE B UFATCLGen: 24  |  Fitness: 27  |  NETKJLMS�LU IS MFKE B UFATCLGen: 25  |  Fitness: 23  |  LETIKOMS LU IS IIKE D WEDQEKGen: 26  |  Fitness: 22  |  NETIKMJS LU IS IIKE D WEDQEKGen: 27  |  Fitness: 20  |  LETIKOMS LU IS KILE B"WFATCLGen: 28  |  Fitness: 19  |  NESGJQJS�GU IS KIKE B WFATEKGen: 29  |  Fitness: 16  |  NETIHPMS KR IS KIKE B WFATEKGen: 30  |  Fitness: 15  |  NESHLPKS KU IS KIKE B WFATEKGen: 31  |  Fitness: 13  |  NETGGNKS KU IS KIKE C WFATEKGen: 32  |  Fitness: 12  |  NETHGNJS IU IS JIKE B WFATCLGen: 33  |  Fitness: 11  |  NETIJPKS IU IS KIKE B WFATEKGen: 35  |  Fitness: 8  |  LEUIHNJS IT IS JIKE A WEATELGen: 37  |  Fitness: 7  |  NETIHNJS IS IS LIKE B WFASELGen: 38  |  Fitness: 6  |  NETHGNJS IT IS LIKE A WFASEKGen: 39  |  Fitness: 4  |  METGHNKS IT IS LIKE B WEATELGen: 42  |  Fitness: 3  |  NETHINKS IT IS KIKE B WEASELGen: 43  |  Fitness: 2  |  NETHINKS IT IS LIKE A WFASELGen: 44  |  Fitness: 1  |  METHHNKS IT IS LIKE A WEASELGen: 46  |  Fitness: 0  |  METHINKS IT IS LIKE A WEASELElapsed time is 0.099618 seconds. `

## Nim

Translation of: Python
`import math, osrandomize() const  target = "METHINKS IT IS LIKE A WEASEL"  alphabet = " ABCDEFGHIJLKLMNOPQRSTUVWXYZ"  p = 0.05  c = 100 proc random(a: string): char = a[random(a.low..a.len)] proc negFitness(trial): int =  for i in 0 .. <trial.len:    if target[i] != trial[i]: inc result proc mutate(parent): string =  result = ""  for c in parent: result.add if random(1.0) < p: random(alphabet) else: c var parent = ""for i in 1..target.len: parent.add random(alphabet) var i = 0while parent != target:  var copies = newSeq[string](c)  for i in 0 .. <copies.len: copies[i] = mutate(parent)   var best = copies[0]  for i in 1 .. <copies.len:    if negFitness(copies[i]) < negFitness(best): best = copies[i]  parent = best   echo i, " ", parent  inc i`

Sample output:

```0 DDTAXEPAFNI RIKNLUBKPXKBFHGA
1 DDTJXEPAFNI RIKNLUB PXKBFHGA
2 CDTJXEPAFNI RI NLUB ZXKBFHGA
3 CDTJXEPAFNI RI KLUB ZXKEFHGA
[...]
37 METJINKS IT IS LIBE A WEANEL
[...]
70 MET INKS IT IS LIKE A WEASEL
71 METHINKS IT IS LIKE A WEASEL```

## Objeck

Translation of: Java
`bundle Default {  class Evolutionary {    target : static : String;    possibilities : static : Char[];    C : static : Int;    minMutateRate : static : Float;    perfectFitness : static : Int;    parent : static : String ;    rand : static : Float;     function : Init() ~ Nil {      target := "METHINKS IT IS LIKE A WEASEL";      possibilities := "ABCDEFGHIJKLMNOPQRSTUVWXYZ "->ToCharArray();      C := 100;      minMutateRate := 0.09;      perfectFitness := target->Size();    }     function : fitness(trial : String) ~ Int {        retVal := 0;         each(i : trial) {          if(trial->Get(i) = target->Get(i)) {            retVal += 1;          };        };         return retVal;      }       function : newMutateRate() ~ Float {        x : Float := perfectFitness - fitness(parent);        y : Float := perfectFitness->As(Float) * (1.01 - minMutateRate);          return x / y;      }       function : mutate(parent : String, rate : Float) ~ String {        retVal := "";         each(i : parent) {          rand := Float->Random();          if(rand <= rate) {                    rand *= 1000.0;            intRand := rand->As(Int);            index : Int := intRand % possibilities->Size();            retVal->Append(possibilities[index]);          }          else {                retVal->Append(parent->Get(i));          };      };         return retVal;      }       function : Main(args : String[]) ~ Nil {        Init();        parent := mutate(target, 1.0);         iter := 0;        while(target->Equals(parent) <> true) {          rate := newMutateRate();          iter += 1;           if(iter % 100 = 0){            IO.Console->Instance()->Print(iter)->Print(": ")->PrintLine(parent);          };           bestSpawn : String;          bestFit := 0;           for(i := 0; i < C; i += 1;) {            spawn := mutate(parent, rate);            fitness := fitness(spawn);             if(fitness > bestFit) {              bestSpawn := spawn;              bestFit := fitness;            };          };           if(bestFit > fitness(parent)) {            parent := bestSpawn;          };          };        parent->PrintLine();      }      }  }}`

Output:

```100: DETHILBMDEB QR YIEGYEBWCCSBN
200: D THIWTXEXH IO SVUDHEEWQASEL
300: DVTHINTILS RIO SVGEKNEWEASEU
400: MFTH AWBLIXNIE STFE AWWEASEJ
500: MFTHIAWDIIRMIY QTFE AWWEASEJ
600: MZTCIAKDQIRNIY NWFE A WEASEJ
700: MZTCIAKDQIRNIY NWFE A WEASEJ
800: MZTCIAKDQIRNIY NWFE A WEASEJ
900: MZTCIAKOWIRNIY NILE A WEASEJ
1000: MZTCIAKOWIRNIY NILE A WEASEJ
1100: MZTCIAKOWIRNIY NILE A WEASEJ
1200: MZTCIAKOWIRNIY NILE A WEASEJ
1300: METCITKSTIRSIY JYKE A WDASEJ
1400: METHITKSTIJ IB FYKE A WDASEJ
1500: METHINKSZIJ IB FYKE A WEASEQ
METHINKS IT IS LIKE A WEASEL```

## OCaml

`let target = "METHINKS IT IS LIKE A WEASEL"let charset = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "let tlen = String.length targetlet clen = String.length charsetlet () = Random.self_init() let parent =  let s = String.create tlen in  for i = 0 to tlen-1 do    s.[i] <- charset.[Random.int clen]  done;  s let fitness ~trial =  let rec aux i d =    if i >= tlen then d else      aux (i+1) (if target.[i] = trial.[i] then d+1 else d) in  aux 0 0 let mutate parent rate =  let s = String.copy parent in  for i = 0 to tlen-1 do    if Random.float 1.0 > rate then      s.[i] <- charset.[Random.int clen]  done;  s, fitness s let () =  let i = ref 0 in  while parent <> target do    let pfit = fitness parent in    let rate = float pfit /. float tlen in    let tries = Array.init 200 (fun _ -> mutate parent rate) in    let min_by (a, fa) (b, fb) = if fa > fb then a, fa else b, fb in    let best, f = Array.fold_left min_by (parent, pfit) tries in    if !i mod 100 = 0 then      Printf.printf "%5d - '%s'  (fitness:%2d)\n%!" !i best f;    String.blit best 0 parent 0 tlen;    incr i  done;  Printf.printf "%5d - '%s'\n" !i parent`

## Octave

Translation of: R
`global target;target = split("METHINKS IT IS LIKE A WEASEL", "");charset = ["A":"Z", " "];p = ones(length(charset), 1) ./ length(charset);parent = discrete_rnd(charset, p, length(target), 1);mutaterate = 0.1; C = 1000; function r = fitness(parent, target)  r = sum(parent == target) ./ length(target);endfunction function r = mutate(parent, mutaterate, charset)  r = parent;  p = unifrnd(0, 1, length(parent), 1);  nmutants = sum( p < mutaterate );  if (nmutants)    s = discrete_rnd(charset, ones(length(charset), 1) ./ length(charset),nmutants,1);    r( p < mutaterate ) = s;  endifendfunction function r = evolve(parent, mutatefunc, fitnessfunc, C, mutaterate, charset)  global target;  children = [];  for i = 1:C    children = [children, mutatefunc(parent, mutaterate, charset)];  endfor  children = [parent, children];  fitval = [];  for i = 1:columns(children)    fitval = [fitval, fitnessfunc(children(:,i), target)];  endfor  [m, im] = max(fitval);  r = children(:, im);endfunction function printgen(p, t, i)  printf("%3d %5.2f %s\n", i, fitness(p, t), p');endfunction i = 0; while( !all(parent == target) )  i++;  parent = evolve(parent, @mutate, @fitness, C, mutaterate, charset);  if ( mod(i, 1) == 0 )    printgen(parent, target, i);  endifendwhiledisp(parent');  `

## Oforth

`200 Constant new: C  5 Constant new: RATE : randChar  // -- c   27 rand dup 27 == ifTrue: [ drop ' ' ] else: [ 'A' + 1- ] ; : fitness(a b -- n)    a b zipWith(#==) sum ; : mutate(s -- s')     s map(#[ 100 rand RATE <= ifTrue: [ drop randChar ] ]) charsAsString ;  : evolve(target)| parent |   ListBuffer init(target size, #randChar) charsAsString ->parent    1 while ( parent target <> ) [       ListBuffer init(C, #[ parent mutate ]) dup add(parent)      maxFor(#[ target fitness ]) dup ->parent . dup println 1+      ] drop ;`
Output:
```>evolve("METHINKS IT IS LIKE A WEASEL")
WHQHNXXAWACZKTTIHKVBCYLPATN  1
WHQHNXXAWACZKTTIHKV CYLPATN  2
WHQHNXXAWACZKTTIHKV C LPATC  3
WHQHNXXSWATZKTTIHKV C LPATC  4
WHQHNXXSWATCKTTIHKV C LEATC  5
WHQHNXXSWATCKTTIHKV C LEATCL 6
WHQHNXXSWATCKFTIHKV C LEASCL 7
WHQHNXXSWATCKF IHKV C LEASCL 8
WHQHNXXSWATZKF IHKV A LEASCL 9
MHQHNXXSWATZKF IHKV A LEASCL 10
MATHNXXSWATZKF ICKV A LEASCL 11
MATHIXXSBATZKF ICKV A LEASCL 12
MATHIXXSBATZKS ICKV A LEASCL 13
MATHIXXSBATZKS BCKV A LEASCL 14
MATHIXXSBATZKS LCKV A LEASCL 15
MATHIXXS ATZKS LSKV A LEASCL 16
MATHIXXS ATJKS LSKV A LEASEL 17
METHIXXS ATJKS LSKV A LEASEL 18
METHIXXS ATJKS LSKE A LEASEL 19
METHINXS ATJKS LSKE A LEASEL 20
METHINXS ATJKS LSKE A WEASEL 21
METHINKS ATJKS LSKE A WEASEL 22
METHINKS ATJUS LSKE A WEASEL 23
METHINKS ATJUS LSKE A WEASEL 24
METHINKS ATJIS LSKE A WEASEL 25
METHINKS ATJIS LSKE A WEASEL 26
METHINKS ATJIS LIKE A WEASEL 27
METHINKS ATJIS LIKE A WEASEL 28
METHINKS STJIS LIKE A WEASEL 29
METHINKS STJIS LIKE A WEASEL 30
METHINKS OT IS LIKE A WEASEL 31
METHINKS OT IS LIKE A WEASEL 32
METHINKS OT IS LIKE A WEASEL 33
METHINKS OT IS LIKE A WEASEL 34
METHINKS OT IS LIKE A WEASEL 35
METHINKS IT IS LIKE A WEASEL 36
ok
```

## OoRexx

Run with Open Object Rexx 4.1.0 by IBM Corporation 1995,2004 Rexx LA 2005-2010. Host OS: Microsoft Windows 7.

` /* Weasel.rex - Me thinks thou art a weasel. - G,M.D. - 2/25/2011 */arg C M/* C is the number of children parent produces each generation. *//* M is the mutation rate of each gene (character) */ call initializegeneration = 0do until parent = target   most_fitness = fitness(parent)   most_fit     = parent   do C      child = mutate(parent, M)      child_fitness = fitness(child)      if child_fitness > most_fitness then      do         most_fitness = child_fitness         most_fit = child         say "Generation" generation": most fit='"most_fit"', fitness="left(most_fitness,4)      end   end   parent = most_fit   generation = generation + 1endexit initialize:   target   = "METHINKS IT IS LIKE A WEASEL"   alphabet = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "   c_length_target = length(target)   parent  = mutate(copies(" ", c_length_target), 1.0)   do i = 1 to c_length_target      target_ch.i = substr(target,i,1)   endreturn fitness: procedure expose target_ch. c_length_target   arg parm_string   fitness = 0   do i_target = 1 to c_length_target      if substr(parm_string,i_target,1) = target_ch.i_target then         fitness = fitness + 1   endreturn fitness mutate:procedure expose alphabetarg string, parm_mutation_rate   result = ""   do istr = 1 to length(string)      if random(1,1000)/1000 <= parm_mutation_rate then         result = result || substr(alphabet,random(1,length(alphabet)),1)      else         result = result || substr(string,istr,1)   endreturn result `

Output:

```C:\usr\rex>weasel 10 .01
Generation 20, most fit='BZTACOQCQ CTMPIXPXBVKRUCLY F', fitness=1
Generation 30, most fit='BZTHCOQCQ CTMPIXPXBVKRUCLY F', fitness=2
Generation 34, most fit='BZTHCOQSQ CTMPIXPXBVKRUCLY F', fitness=3
Generation 61, most fit='BZTHCOQSQ CTIPIXPXBVKRUCLY F', fitness=4
Generation 95, most fit='BZTHCNQSQ CTIPIXPXBVKRUCLY F', fitness=5
Generation 107, most fit='BZTHCNQSQ CTISIXPXBVKRUCLY F', fitness=6
Generation 121, most fit='BZTHCNQS  CTISIXPXBVKRUCLY F', fitness=7
Generation 129, most fit='BZTHCNQS  CTISIXPXBVKRUELY F', fitness=8
Generation 142, most fit='BZTHCNQS  CTISIXPXBVKRUELS F', fitness=9
Generation 143, most fit='BZTHCNQS ICTISIXPXBVKRUEHS F', fitness=10
Generation 147, most fit='BZTHCNQS ICTISIXPXBVKRUEHS L', fitness=11
Generation 154, most fit='BZTHCNQS IC ISIXPXBVKRUEHS L', fitness=12
Generation 201, most fit='BZTHCNQS IT ISIXPXBVKRUEHS L', fitness=13
Generation 213, most fit='BZTHCNQS IT ISIXPXEVKRUEHS L', fitness=14
Generation 250, most fit='BZTHCNKS IT ISIXPXEVKRUEHS L', fitness=15
Generation 268, most fit='BZTHCNKS IT ISIXPXEVKFUEAS L', fitness=16
Generation 274, most fit='BZTHCNKS IT ISIXPKEVKFUEAS L', fitness=17
Generation 292, most fit='BZTHCNKS IT ISIXPKEVKFWEAS L', fitness=18
Generation 353, most fit='BZTHCNKS IT ISIXPKEVKFWEASEL', fitness=19
Generation 358, most fit='BZTHCNKS IT ISIXPKEVK WEASEL', fitness=20
Generation 374, most fit='BETHCNKS IT ISIXPKEVK WEASEL', fitness=21
Generation 404, most fit='BETHCNKS IT ISILPKEVK WEASEL', fitness=22
Generation 405, most fit='BETHCNKS IT ISILPKE K WEASEL', fitness=23
Generation 448, most fit='FETHCNKS IT ISILPKE A WEASEL', fitness=24
Generation 679, most fit='FETHINKS IT ISILPKE A WEASEL', fitness=25
Generation 964, most fit='METHINKS IT ISILPKE A WEASEL', fitness=26
Generation 1018, most fit='METHINKS IT ISILIKE A WEASEL', fitness=27
Generation 1250, most fit='METHINKS IT IS LIKE A WEASEL', fitness=28

C:\usr\rex>
```

## OxygenBasic

The algorithm pared down to the essentials. It takes around 1200 to 6000 mutations to attain the target. Fitness is measured by the number of beneficial mutations. The cycle ends when this is equal to the string length.

`  'EVOLUTION target="METHINKS IT IS LIKE A WEASEL"le=len targetprogeny=string le,"X" quad seeddeclare QueryPerformanceCounter lib "kernel32.dll" (quad*q)QueryPerformanceCounter seed Function Rand(sys max) as sys  mov    eax,max  inc    eax  imul   edx,seed,0x8088405  inc    edx  mov    seed,edx  mul    edx  return edxEnd Function sys ls=le-1,cp=0,ct=0,ch=0,fit=0,gens=0 do                         '1 mutation per generation  i=1+rand ls              'mutation position  ch=64+rand 26            'mutation ascii code  if ch=64 then ch=32      'change '@' to ' '  ct=asc target,i          'target ascii code  cp=asc progeny,i         'parent ascii code  '  if ch=ct then    if cp<>ct then      mid progeny,i,chr ch 'carry improvement      fit++                'increment fitness    end if  end if  gens++  if fit=le then exit do   'matches targetend doprint progeny "  " gens 'RESULT (range 1200-6000 generations) `

## Oz

`declare  Target = "METHINKS IT IS LIKE A WEASEL"  C = 100  MutateRate = 5 %% percent   proc {Main}     X0 = {MakeN {Length Target} RandomChar}  in     for Xi in {Iterate Evolve X0} break:Break do        {System.showInfo Xi}        if Xi == Target then {Break} end     end  end   fun {Evolve Xi}     Copies = {MakeN C fun {\$} {Mutate Xi} end}  in     {FoldL Copies MaxByFitness Xi}  end   fun {Mutate Xs}     {Map Xs      fun {\$ X}         if {OS.rand} mod 100 < MutateRate then {RandomChar}         else X         end      end}  end   fun {MaxByFitness A B}     if {Fitness B} > {Fitness A} then B else A end   end   fun {Fitness Candidate}     {Length {Filter {List.zip Candidate Target Value.'=='} Id}}  end   Alphabet = & |{List.number &A &Z 1}  fun {RandomChar}     I = {OS.rand} mod {Length Alphabet} + 1  in     {Nth Alphabet I}  end   %% General purpose helpers   fun {Id X} X end   fun {MakeN N F}     Xs = {List.make N}  in     {ForAll Xs F}     Xs  end   fun lazy {Iterate F X}     X|{Iterate F {F X}}  endin  {Main}`

## PARI/GP

The algorithm given here is more general than the one described, in which letters can be inserted or deleted as well as mutated. The rate for insertions and deletions are set to 0, however, so the results are the same.

This code is inefficient (tens of milliseconds) since it converts back and forth between string and vector format. A more efficient version would keep the information in a Vecsmall instead.

`target="METHINKS IT IS LIKE A WEASEL";fitness(s)=-dist(Vec(s),Vec(target));dist(u,v)=sum(i=1,min(#u,#v),u[i]!=v[i])+abs(#u-#v);letter()=my(r=random(27)); if(r==26, " ", Strchr(r+65));insert(v,x=letter())={	my(r=random(#v+1));	if(r==0, return(concat([x],v)));	if(r==#v, return(concat(v,[x])));	concat(concat(v[1..r],[x]),v[r+1..#v]);}delete(v)={	if(#v<2, return([]));	my(r=random(#v)+1);	if(r==1, return(v[2..#v]));	if(r==#v, return(v[1..#v-1]));	concat(v[1..r-1],v[r+1..#v]);}mutate(s,rateM,rateI,rateD)={	my(v=Vec(s));	if(random(1.)<rateI, v=insert(v));	if(random(1.)<rateD, v=delete(v));	for(i=1,#v,		if(random(1.)<rateM, v[i]=letter())	);	concat(v);}evolve(C,rate)={	my(parent=concat(vector(#target,i,letter())),ct=0);	while(parent != target,		print(parent" "fitness(parent));		my(v=vector(C,i,mutate(parent,rate,0,0)),best,t);		best=fitness(parent=v[1]);		for(i=2,C,			t=fitness(v[i]);			if(t>best, best=t; parent=v[i])		);		ct++	);	print(parent" "fitness(parent));	ct;}evolve(35,.05)`

## Pascal

This Pascal version of the program displays the initial random string and every hundredth generation after that. It also displays the final generation count. Mutation happens relatively slowly, about once in every 1000 characters, but this can be changed by altering the RATE constant. Lower values for RATE actually speed up the mutations.

`PROGRAM EVOLUTION (OUTPUT); CONST	TARGET = 'METHINKS IT IS LIKE A WEASEL';	COPIES = 100;  (* 100 children in each generation. *)	RATE = 1000;  (* About one character in 1000 will be a mutation. *) TYPE	STRLIST = ARRAY [1..COPIES] OF STRING; FUNCTION RANDCHAR : CHAR; (* Generate a random letter or space. *) VAR RANDNUM : INTEGER; BEGIN	RANDNUM := RANDOM(27);	IF RANDNUM = 26 THEN		RANDCHAR := ' '	ELSE		RANDCHAR := CHR(RANDNUM + ORD('A')) END; FUNCTION RANDSTR (SIZE : INTEGER) : STRING; (* Generate a random string. *) VAR	N : INTEGER;	S : STRING; BEGIN	S := '';	FOR N := 1 TO SIZE DO		INSERT(RANDCHAR, S, 1);	RANDSTR := S END; FUNCTION FITNESS (CANDIDATE, GOAL : STRING) : INTEGER; (* Count the number of correct letters in the correct places *) VAR N, MATCHES : INTEGER; BEGIN	MATCHES := 0;	FOR N := 1 TO LENGTH(GOAL) DO		IF CANDIDATE[N] = GOAL[N] THEN			MATCHES := MATCHES + 1;	FITNESS := MATCHES END; FUNCTION MUTATE (RATE : INTEGER; S : STRING) : STRING; (* Randomly alter a string. Characters change with probability 1/RATE. *) VAR	N : INTEGER;	CHANGE : BOOLEAN; BEGIN	FOR N := 1 TO LENGTH(TARGET) DO	 BEGIN		CHANGE := RANDOM(RATE) = 0;		IF CHANGE THEN			S[N] := RANDCHAR	 END;	MUTATE := S END; PROCEDURE REPRODUCE (RATE : INTEGER; PARENT : STRING; VAR CHILDREN : STRLIST); (* Generate children with random mutations. *) VAR N : INTEGER; BEGIN	FOR N := 1 TO COPIES DO		CHILDREN[N] := MUTATE(RATE, PARENT) END; FUNCTION FITTEST(CHILDREN : STRLIST; GOAL : STRING) : STRING; (* Measure the fitness of each child and return the fittest. *) (* If multiple children equally match the target, then return the first. *) VAR	MATCHES, MOST_MATCHES, BEST_INDEX, N : INTEGER; BEGIN	MOST_MATCHES := 0;	BEST_INDEX := 1;	FOR N := 1 TO COPIES DO	 BEGIN		MATCHES := FITNESS(CHILDREN[N], GOAL);		IF MATCHES > MOST_MATCHES THEN		 BEGIN			MOST_MATCHES := MATCHES;			BEST_INDEX := N		 END	 END;	FITTEST := CHILDREN[BEST_INDEX] END; VAR	PARENT, BEST_CHILD : STRING;	CHILDREN : STRLIST;	GENERATIONS : INTEGER; BEGIN	RANDOMIZE;	GENERATIONS := 0;	PARENT := RANDSTR(LENGTH(TARGET));	WHILE NOT (PARENT = TARGET) DO	 BEGIN		IF (GENERATIONS MOD 100) = 0 THEN WRITELN(PARENT);		GENERATIONS := GENERATIONS + 1;		REPRODUCE(RATE, PARENT, CHILDREN);		BEST_CHILD := FITTEST(CHILDREN, TARGET);		IF FITNESS(PARENT, TARGET) < FITNESS(BEST_CHILD, TARGET) THEN			PARENT := BEST_CHILD	 END;	WRITE('The string was matched in ');	WRITELN(GENERATIONS, ' generations.')END.`

## Perl

This implementation usually converges in less than 70 iterations.

`use List::Util 'reduce';use List::MoreUtils 'false'; ### Generally useful declarations sub randElm {\$_[int rand @_]} sub minBy (&@) {my \$f = shift;  reduce {\$f->(\$b) < \$f->(\$a) ? \$b : \$a} @_;} sub zip {@_ or return ();  for (my (\$n, @a) = 0 ;; ++\$n)    {my @row;     foreach (@_)        {\$n < @\$_ or return @a;         push @row, \$_->[\$n];}     push @a, \@row;}} ### Task-specific declarations my \$C = 100;my \$mutation_rate = .05;my @target = split '', 'METHINKS IT IS LIKE A WEASEL';my @valid_chars = (' ', 'A' .. 'Z'); sub fitness {false {\$_->[0] eq \$_->[1]} zip shift, \@target;} sub mutate {my \$rate = shift;  return [map {rand() < \$rate ? randElm @valid_chars : \$_} @{shift()}];} ### Main loop my \$parent = [map {randElm @valid_chars} @target]; while (fitness \$parent)   {\$parent =       minBy \&fitness,       map {mutate \$mutation_rate, \$parent}       1 .. \$C;    print @\$parent, "\n";}`

## Perl 6

Works with: rakudo version 2015-11-11
`constant target = "METHINKS IT IS LIKE A WEASEL";constant mutate_chance = .08;constant @alphabet = flat 'A'..'Z',' ';constant C = 100; sub mutate { [~] (rand < mutate_chance ?? @alphabet.pick !! \$_ for \$^string.comb) }sub fitness { [+] \$^string.comb Zeq state @ = target.comb } loop (    my \$parent = @alphabet.roll(target.chars).join;    \$parent ne target;    \$parent = max :by(&fitness), mutate(\$parent) xx C) { printf "%6d: '%s'\n", \$++, \$parent }`

## Phix

`constant target = "METHINKS IT IS LIKE A WEASEL",         AZS    = "ABCDEFGHIJKLMNOPQRSTUVWXYZ ",         C = 5000,  -- children in each generation         P = 15     -- probability of mutation (1 in 15) function fitness(string sample, string target)    return sum(sq_eq(sample,target))end function function mutate(string s, integer n)    for i=1 to length(s) do        if rand(n)=1 then            s[i] = AZS[rand(length(AZS))]        end if    end for    return send function string parent = mutate(target,1) -- (mutate with 100% probability)sequence samples = repeat(0,C)integer gen = 0, best, fit, best_fit = fitness(parent,target)while parent!=target do    printf(1,"Generation%3d: %s, fitness %3.2f%%\n", {gen, parent, (best_fit/length(target))*100})    best_fit = -1    for i=1 to C do        samples[i] = mutate(parent, P)        fit = fitness(samples[i], target)        if fit > best_fit then            best_fit = fit            best = i        end if    end for    parent = samples[best]    gen += 1end whileprintf(1,"Finally, \"%s\"\n",{parent})`
Output:
```Generation  0: NKY NWLYBJOPOJFE RRISKGJD RS, fitness 0.00%
Generation  1: NKYHNNLYAIOPOJFE ERISKGJD RS, fitness 10.71%
Generation  2: NKYHNNLYAIOPOJFEIER SKGJD RS, fitness 17.86%
Generation  3: IKYHNNLSAIOPOJFLIER SKGJW RS, fitness 25.00%
Generation  4: MKTHNNLSAIOPOJILIER SKGJW RS, fitness 32.14%
Generation  5: MKTHNNLSAITFOJILIEE SKGJW RS, fitness 39.29%
Generation  6: MKTHONLSAITFOJILIEE SKGJW EL, fitness 46.43%
Generation  7: MKTHINLSAITFIJILIIE SKJJW EL, fitness 53.57%
Generation  8: MKTHINLSAITFIS LIIE SKJJW EL, fitness 60.71%
Generation  9: MKTHINLSAITFIS LIKE AKJJW EL, fitness 67.86%
Generation 10: MKTHINLSAITFIS LIKE AKJEA EL, fitness 75.00%
Generation 11: METHINLSAIT IS LIKE AKJEA EL, fitness 82.14%
Generation 12: METHINLSAIT IS LIKE AKWEA EL, fitness 85.71%
Generation 13: METHINLS IT IS LIKE AKWEA EL, fitness 89.29%
Generation 14: METHINLS IT IS LIKE A WEA EL, fitness 92.86%
Generation 15: METHINLS IT IS LIKE A WEASEL, fitness 96.43%
Finally, "METHINKS IT IS LIKE A WEASEL"
```

## PicoLisp

This example uses 'gen', the genetic function in "lib/simul.l"

`(load "@lib/simul.l") (setq *Target (chop "METHINKS IT IS LIKE A WEASEL")) # Generate random character(de randChar ()   (if (=0 (rand 0 26))      " "      (char (rand `(char "A") `(char "Z"))) ) ) # Fitness function (Hamming distance)(de fitness (A)   (cnt = A *Target) ) # Genetic algorithm(gen   (make                               # Parent population      (do 100                             # C = 100 children         (link            (make               (do (length *Target)                  (link (randChar)) ) ) ) ) )   '((A)                               # Termination condition      (prinl (maxi fitness A))            # Print the fittest element      (member *Target A) )                # and check if solution is found   '((A B)                             # Recombination function      (mapcar         '((C D) (if (rand T) C D))       # Pick one of the chars         A B ) )   '((A)                               # Mutation function      (mapcar         '((C)            (if (=0 (rand 0 10))          # With a proability of 10%               (randChar)                 # generate a new char, otherwise               C ) )                      # return the current char         A ) )   fitness )                           # Selection function`

Output:

```RQ ASLWWWI ANSHPNABBAJ ZLTKX
DETGGNGHWITIKSXLIIEBA WAATPC
CETHINWS ITKESQGIKE A WSAGHO
METHBNWS IT NSQLIKE A WEAEWL
METHINKS IT ISCLIKE A WVASEL
METHINKS IT ISOLIKE A WEASEL
METHINKS IT IS LIKE A WEASEL```

## PHP

` define('TARGET','METHINKS IT IS LIKE A WEASEL');define('TBL','ABCDEFGHIJKLMNOPQRSTUVWXYZ '); define('MUTATE',15);define('COPIES',30);define('TARGET_COUNT',strlen(TARGET));define('TBL_COUNT',strlen(TBL)); // Determine number of different chars between a and b function unfitness(\$a,\$b){        \$sum=0;        for(\$i=0;\$i<strlen(\$a);\$i++)                if(\$a[\$i]!=\$b[\$i]) \$sum++;        return(\$sum);} function mutate(\$a){        \$tbl=TBL;        for(\$i=0;\$i<strlen(\$a);\$i++) \$out[\$i]=mt_rand(0,MUTATE)?\$a[\$i]:\$tbl[mt_rand(0,TBL_COUNT-1)];        return(implode('',\$out));}  \$tbl=TBL;for(\$i=0;\$i<TARGET_COUNT;\$i++) \$tspec[\$i]=\$tbl[mt_rand(0,TBL_COUNT-1)];\$parent[0]=implode('',\$tspec);\$best=TARGET_COUNT+1;\$iters=0;do {        for(\$i=1;\$i<COPIES;\$i++) \$parent[\$i]=mutate(\$parent[0]);         for(\$best_i=\$i=0; \$i<COPIES;\$i++) {                \$unfit=unfitness(TARGET,\$parent[\$i]);                if(\$unfit < \$best || !\$i) {                        \$best=\$unfit;                        \$best_i=\$i;                }        }        if(\$best_i>0) \$parent[0]=\$parent[\$best_i];        \$iters++;        print("Generation \$iters, score \$best: \$parent[0]\n");} while(\$best);  `

Sample Output:

```Generation 1, score 25: IIVHUVOC NRGYBUEXLF LXZ SGMT
Generation 2, score 24: MIVHUVOC MKGYBUEXLF LXZ HGMT
Generation 3, score 24: MIVHUVOC MKGYBUEXLF LXZ HGMT
...
Generation 177, score 1: METHQNKS IT IS LIKE A WEASEL
Generation 178, score 0: METHINKS IT IS LIKE A WEASEL
```

## Pike

C is not used because i found it has no effect on the number of mutations needed to find the solution. in difference to the proposal, rate is not set as a percentage but as the number of characters to mutate when generating an offspring.

the rate is fixed at 2 as that is the lowest most successful rate still in the spirit of the original proposal (where mutation allows a previously successful change to be undone). if the rate is 1 than every successful character change can not change again (because it would not cause an improvement and thus be rejected.)

`string chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "; string mutate(string data, int rate){    array(int) alphabet=(array(int))chars;    multiset index = (multiset)enumerate(sizeof(data));    while(rate)    {          int pos = random(index);        data[pos]=random(alphabet);        rate--;    }    return data;} int fitness(string input, string target){    return `+(@`==(((array)input)[*], ((array)target)[*]));} void main(){    array(string) alphabet = chars/"";    string target = "METHINKS IT IS LIKE A WEASEL";    string parent = "";     while(sizeof(parent) != sizeof(target))    {        parent += random(alphabet);    }     int count;    write(" %5d: %s\n", count, parent);    while (parent != target)    {          string child = mutate(parent, 2);        count++;        if (fitness(child, target) > fitness(parent, target))        {              write(" %5d: %s\n", count, child);            parent = child;        }    }}`

Output:

```    0: TIRABZB IGVG TDXTGODFOXO UPU
2: TIRABZB IGVG TDXTGO FOXOTUPU
32: TIRABZB IGVG T XTGO FOXOTUPU
39: TIRABZB IGVG T JTGO AOXOTUPU
44: TIRABNB IGMG T JTGO AOXOTUPU
57: TIRABNB IGMG T ITGO AOXOTSPU
62: TISHBNB IGMG T ITGO AOXOTSPU
63: TISHBNB IGM  T ITGO AOXONSPU
74: TISHBNB  GM  T ITGO AOHONSPU
89: TISHBNB  GM  S ITGO AYHONSPU
111: TISHBNB  GM  S ITGO AYHOASPU
112: MISHBNB  GM  S ITGO AYHUASPU
145: MISHBNBG IM  S ITGO AYHUASPU
169: MISHBNBG IM NS ITGO AYHEASPU
182: MESHBNBG IM NS ATGO AYHEASPU
257: MESHBNBG ID NS ATGO A HEASPU
320: MESHBNBG ID NS LRGO A HEASPU
939: MESHINBG ID NS LRGO A HEASPU
1134: MESHINBG ID NS LRZO A HEASEU
1264: MESHINBG ID US LIZO A HEASEU
1294: MEYHINBG IT US LIZO A HEASEU
1507: MEYHINBG IT US LIZO A HEASEL
1823: METHINBG IT US LIZO A HEASEL
2080: METHINBG IT US LI E A HEASEL
2143: METHINBG IT IS LI E A HEASEL
3118: METHINWG IT IS LIKE A HEASEL
3260: METHINWC IT IS LIKE A WEASEL
3558: METHINWS IT IS LIKE A WEASEL
4520: METHINKS IT IS LIKE A WEASEL
```

## Pony

Translation of: Java
`use "random" actor Main  let _env: Env  let _rand: MT = MT	// Mersenne Twister  let _target: String = "METHINKS IT IS LIKE A WEASEL"  let _possibilities: String = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "  let _c: U16 = 100	// number of spawn per generation  let _min_mutate_rate: F64 = 0.09  let _perfect_fitness: USize = _target.size()  var _parent: String = ""   new create(env: Env) =>    _env = env    _parent = mutate(_target, 1.0)    var iter: U64 = 0    while not _target.eq(_parent) do      let rate: F64 = new_mutate_rate()      iter = iter + 1      if (iter % 100) == 0 then        _env.out.write(iter.string() + ": " + _parent)        _env.out.write(", fitness: " + fitness(_parent).string())        _env.out.print(", rate: " + rate.string())      end      var best_spawn = ""      var best_fit: USize = 0      var i: U16 = 0      while i < _c do        let spawn = mutate(_parent, rate)        let spawn_fitness = fitness(spawn)        if spawn_fitness > best_fit then          best_spawn = spawn          best_fit = spawn_fitness        end        i = i + 1      end      if best_fit > fitness(_parent) then        _parent = best_spawn      end    end    _env.out.print(_parent + ", " + iter.string())   fun fitness(trial: String): USize =>    var ret_val: USize = 0    var i: USize = 0    while i < trial.size() do      try        if trial(i)? == _target(i)? then          ret_val = ret_val + 1        end      end      i = i + 1    end    ret_val   fun new_mutate_rate(): F64 =>    let perfect_fit = _perfect_fitness.f64()    ((perfect_fit - fitness(_parent).f64()) / perfect_fit) * (1.0 - _min_mutate_rate)   fun ref mutate(parent: String box, rate: F64): String =>    var ret_val = recover trn String end    for char in parent.values() do      let rnd_real: F64 = _rand.real()      if rnd_real <= rate then        let rnd_int: U64 = _rand.int(_possibilities.size().u64())        try          ret_val.push(_possibilities(rnd_int.usize())?)        end      else        ret_val.push(char)      end    end    consume ret_val`

Output:

```100: UMMMDNKR IEIIB IIKZ A THAHEL, fitness: 14, rate: 0.455
200: UMMMDNKR IEIIB IIKZ A THAHEL, fitness: 14, rate: 0.455
300: KMHJZNKS IUIIS IISQ A TWASEL, fitness: 16, rate: 0.39
400: KHHHCNKS IT I  CIKE A XFASEL, fitness: 20, rate: 0.26
500: MINHINKS IT IS LIKE A WEASEL, fitness: 26, rate: 0.065
METHINKS IT IS LIKE A WEASEL, 526```

Alternative solution:
Using a more OO approach that leverages classes for encapsulation.

`use "random"use "collections" class CreationFactory  let _desired: String   new create(d: String) =>    _desired = d   fun apply(c: String): Creation =>    Creation(c, _fitness(c))   fun _fitness(s: String): USize =>    var f = USize(0)    for i in Range(0, s.size()) do      try        if s(i)? == _desired(i)? then          f = f +1        end      end    end    f class val Creation  let string: String  let fitness: USize   new val create(s: String = "", f: USize = 0) =>    string = s    fitness = f class Mutator  embed _rand: MT = MT  let _possibilities: String = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "  let _cf: CreationFactory   new create(cf: CreationFactory) =>    _cf = cf   fun ref apply(parent: Creation, rate: F64): Creation =>    let ns = _new_string(parent.string, rate)    _cf(ns)   fun ref _new_string(parent: String, rate: F64): String =>    var mutated = recover String(parent.size()) end    for char in parent.values() do      mutated.push(_mutate_letter(char, rate))    end    consume mutated   fun ref _mutate_letter(current: U8, rate: F64): U8 =>    if _rand.real() <= rate then      _random_letter()    else      current    end   fun ref _random_letter(): U8 =>    let ln = _rand.int(_possibilities.size().u64()).usize()    try _possibilities(ln)? else ' ' end class Generation  let _size: USize  let _desired: Creation  let _mutator: Mutator   new create(size: USize = 100, desired: Creation, mutator: Mutator) =>    _size = size    _desired = desired    _mutator = consume mutator   fun ref apply(parent: Creation): Creation =>    var best = parent    let mutation_rate = _mutation_rate(best)    for i in Range(0, _size) do      let candidate = _mutator(best, mutation_rate)      if candidate.fitness > best.fitness then        best = candidate      end    end    best   fun _mutation_rate(best: Creation): F64 =>    let min_mutate_rate: F64 = 0.09     let df = _desired.fitness.f64()    let bf = best.fitness.f64()     ((df - bf) / df) * (1.0 - min_mutate_rate) actor Main  new create(env: Env) =>    let d = "METHINKS IT IS LIKE A WEASEL"    let cf = CreationFactory(d)    let desired = cf(d)    let mutator = Mutator(cf)    let start = mutator(desired, 1.0)    let spawn_per_generation = USize(100)     var iterations = U64(0)    var best = start     repeat      best = Generation(spawn_per_generation, desired, mutator)(best)       iterations = iterations + 1      if (iterations % 100) == 0 then        env.out.print(          iterations.string() + ": "          + best.string + ", fitness: " + best.fitness.string()          )      end    until best.string == desired.string end     env.out.print(best.string + ", " + iterations.string())`

Output:

```100: MELWILYSH TDKKTPIKE DXWEASKL, fitness: 14
200: MEMHINTSLLT M KPFKETN WEASHL, fitness: 16
300: MQTHINFS ET MT DIKEVA WEASEL, fitness: 21
400: METHINKS IT IS DIKEDA WEASEL, fitness: 26
METHINKS IT IS LIKE A WEASEL, 442```

## Prolog

Works with: SWI Prolog version 6.2.6 by Jan Wielemaker, University of Amsterdam
`target("METHINKS IT IS LIKE A WEASEL"). rndAlpha(64, 32).     % Generate a single random characterrndAlpha(P, P).	      % 32 is a space, and 65->90 are upper caserndAlpha(Ch) :- random(N), P is truncate(64+(N*27)), !, rndAlpha(P, Ch). rndTxt(0, []).        % Generate some random text (fixed length)rndTxt(Len, [H|T]) :- succ(L, Len), rndAlpha(H), !, rndTxt(L, T). score([], [], Score, Score).   % Score a generated mutation (count diffs)score([Ht|Tt], [Ht|Tp], C, Score) :- !, score(Tt, Tp, C, Score).score([_|Tt], [_|Tp], C, Score) :- succ(C, N), !, score(Tt, Tp, N, Score).score(Txt, Score, Target) :- !, score(Target, Txt, 0, Score). mutate(_, [], []).             % mutate(Probability, Input, Output)mutate(P, [H|Txt], [H|Mut]) :- random(R), R < P, !, mutate(P, Txt, Mut).mutate(P, [_|Txt], [M|Mut]) :- rndAlpha(M), !, mutate(P, Txt, Mut). weasel(Tries, _, _, mutation(0, Result)) :-               % No differences=success	format('~w~4|:~w~3| - ~s\n', [Tries, 0, Result]).weasel(Tries, Chance, Target, mutation(S, Value)) :-	    % output progress	format('~w~4|:~w~3| - ~s\n', [Tries, S, Value]), !, % and call again	weasel(Tries, Chance, Target, Value).weasel(Tries, Chance, Target, Start) :-	findall(mutation(S,M),  % Generate 30 mutations, select the best.		(between(1, 30, _), mutate(Chance, Start, M), score(M,S,Target)),		Mutations),     % List of 30 mutations and their scores	sort(Mutations, [Best|_]), succ(Tries, N),	!, weasel(N, Chance, Target, Best).weasel :-  % Chance->probability for a mutation, T=Target, Start=initial text	target(T), length(T, Len), rndTxt(Len, Start), Chance is 1 - (1/(Len+1)),	!, weasel(0, Chance, T, Start).`

Output:

``` time(weasel).
1   :27 - SGR JDTLWJQNGFOEJNQTVQOJLEEV
2   :27 - SGR DDTLWJQNGFOEJNQTVQOJLEEV
3   :26 - SGR DDTLWJQNGFHEJNQTVQOJLSEV
4   :25 - MGR DDWLWJQNGFHEJDQTVQOJLSEV
5   :24 - MGR DDWL JQNGFHEJDQTVQOJLSEV
6   :24 - MGR DBWL JQNGFHEJUQTVQOJLSEV
7   :23 - MRR IBWL JQNGFHEJUQTVFOJLSEV
...
168 :1 - METHINKS IT I  LIKE A WEASEL
169 :1 - METHINKS IT I  LIKE A WEASEL
170 :1 - METHINKS IT I  LIKE A WEASEL
171 :1 - METHINKS IT I  LIKE A WEASEL
172 :1 - METHINKS IT I  LIKE A WEASEL
173 :0 - METHINKS IT IS LIKE A WEASEL
% 810,429 inferences, 0.125 CPU in 0.190 seconds (66% CPU, 6493780 Lips)
true ```

## PureBasic

`Define population = 100, mutationRate = 6Define.s target\$ = "METHINKS IT IS LIKE A WEASEL"Define.s charSet\$ = "ABCDEFGHIJKLMNOPQRSTUVWXYZ " Procedure.i fitness(Array aspirant.c(1), Array target.c(1))  Protected i, len, fit   len = ArraySize(aspirant())  For i = 0 To len    If aspirant(i) = target(i): fit +1: EndIf  Next   ProcedureReturn fitEndProcedure Procedure mutatae(Array parent.c(1), Array child.c(1), Array charSetA.c(1), rate.i)  Protected i, L, maxC  L = ArraySize(child())  maxC = ArraySize(charSetA())  For i = 0 To L    If Random(100) < rate      child(i) = charSetA(Random(maxC))    Else       child(i) = parent(i)    EndIf     NextEndProcedure Procedure.s cArray2string(Array A.c(1))  Protected S.s, len  len = ArraySize(A())+1 : S = Space(len)   CopyMemory(@A(0), @S, len * SizeOf(Character))  ProcedureReturn SEndProcedure Define mutationRate, maxChar, target_len, i, maxfit, gen, fit, bestfit Dim targetA.c(Len(target\$) - 1)CopyMemory(@target\$, @targetA(0), StringByteLength(target\$)) Dim charSetA.c(Len(charSet\$) - 1)CopyMemory(@charSet\$, @charSetA(0), StringByteLength(charSet\$)) maxChar   = Len(charSet\$) - 1maxfit = Len(target\$)target_len   = Len(target\$) - 1Dim    parent.c(target_len)Dim     child.c(target_len)Dim Bestchild.c(target_len)  For i = 0 To target_len  parent(i) = charSetA(Random(maxChar))Next fit = fitness (parent(), targetA())OpenConsole() PrintN(Str(gen) + ": " + cArray2string(parent()) + ": Fitness= " + Str(fit) + "/" + Str(maxfit)) While bestfit <> maxfit  gen + 1  For i = 1 To population    mutatae(parent(),child(),charSetA(), mutationRate)    fit = fitness (child(), targetA())    If fit > bestfit      bestfit = fit: CopyArray(child(), Bestchild())    EndIf     Next  CopyArray(Bestchild(), parent())  PrintN(Str(gen) + ": " + cArray2string(parent()) + ": Fitness= " + Str(bestfit) + "/" + Str(maxfit)) Wend  PrintN("Press any key to exit"): Repeat: Until Inkey() <> ""`

## Python

Using lists instead of strings for easier manipulation, and a mutation rate that gives more mutations the further the parent is away from the target.

`from string import lettersfrom random import choice, random target  = list("METHINKS IT IS LIKE A WEASEL")charset = letters + ' 'parent  = [choice(charset) for _ in range(len(target))]minmutaterate  = .09C = range(100) perfectfitness = float(len(target)) def fitness(trial):    'Sum of matching chars by position'    return sum(t==h for t,h in zip(trial, target)) def mutaterate():    'Less mutation the closer the fit of the parent'    return 1-((perfectfitness - fitness(parent)) / perfectfitness * (1 - minmutaterate)) def mutate(parent, rate):    return [(ch if random() <= rate else choice(charset)) for ch in parent] def que():    '(from the favourite saying of Manuel in Fawlty Towers)'    print ("#%-4i, fitness: %4.1f%%, '%s'" %           (iterations, fitness(parent)*100./perfectfitness, ''.join(parent))) def mate(a, b):    place = 0    if choice(xrange(10)) < 7:        place = choice(xrange(len(target)))    else:        return a, b     return a, b, a[:place] + b[place:], b[:place] + a[place:] iterations = 0center = len(C)/2while parent != target:    rate = mutaterate()    iterations += 1    if iterations % 100 == 0: que()    copies = [ mutate(parent, rate) for _ in C ]  + [parent]    parent1 = max(copies[:center], key=fitness)    parent2 = max(copies[center:], key=fitness)    parent = max(mate(parent1, parent2), key=fitness)que()`

Sample output

```#100 , fitness: 50.0%, 'DVTAIKKS OZ IAPYIKWXALWE CEL'
#200 , fitness: 60.7%, 'MHUBINKMEIG IS LIZEVA WEOPOL'
#300 , fitness: 71.4%, 'MEYHINKS ID SS LIJF A KEKUEL'

#378 , fitness: 100.0%, 'METHINKS IT IS LIKE A WEASEL'```

A simpler Python version that converges in less steps:

`from random import choice, random target  = list("METHINKS IT IS LIKE A WEASEL")alphabet = " ABCDEFGHIJLKLMNOPQRSTUVWXYZ"p = 0.05 # mutation probabilityc = 100  # number of children in each generation def neg_fitness(trial):    return sum(t != h for t,h in zip(trial, target)) def mutate(parent):    return [(choice(alphabet) if random() < p else ch) for ch in parent] parent = [choice(alphabet) for _ in xrange(len(target))]i = 0print "%3d" % i, "".join(parent)while parent != target:    copies = (mutate(parent) for _ in xrange(c))    parent = min(copies, key=neg_fitness)    print "%3d" % i, "".join(parent)    i += 1`

## R

`set.seed(1234, kind="Mersenne-Twister") ## Easier if the string is a character vectortarget <- unlist(strsplit("METHINKS IT IS LIKE A WEASEL", "")) charset <- c(LETTERS, " ")parent <- sample(charset, length(target), replace=TRUE) mutaterate <- 0.01 ## Number of offspring in each generationC <- 100 ## Hamming distance between strings normalized by string length is used## as the fitness function.fitness <- function(parent, target) {    sum(parent == target) / length(target)} mutate <- function(parent, rate, charset) {    p <- runif(length(parent))    nMutants <- sum(p < rate)    if (nMutants) {        parent[ p < rate ] <- sample(charset, nMutants, replace=TRUE)    }    parent} evolve <- function(parent, mutate, fitness, C, mutaterate, charset) {    children <- replicate(C, mutate(parent, mutaterate, charset),                          simplify=FALSE)    children <- c(list(parent), children)    children[[which.max(sapply(children, fitness, target=target))]]} .printGen <- function(parent, target, gen) {    cat(format(i, width=3),        formatC(fitness(parent, target), digits=2, format="f"),        paste(parent, collapse=""), "\n")} i <- 0.printGen(parent, target, i)while ( ! all(parent == target)) {    i <- i + 1    parent <- evolve(parent, mutate, fitness, C, mutaterate, charset)     if (i %% 20 == 0) {        .printGen(parent, target, i)    }}.printGen(parent, target, i)`

output:

```  0 0.00 DQQQXRAGRNSOHYHWHHFGIIEBFVOY
20 0.36 MQQQXBAS TTOHSHLHKF I ABFSOY
40 0.71 MQTHINKS TTXHSHLIKE A WBFSEY
60 0.82 METHINKS IT HSHLIKE A WBFSEY
80 0.93 METHINKS IT HS LIKE A WEFSEL
99 1.00 METHINKS IT IS LIKE A WEASEL
```

### Alternative

Very close to former solution, but a bit easier.

`# Setupset.seed(42)target= unlist(strsplit("METHINKS IT IS LIKE A WEASEL", ""))chars= c(LETTERS, " ")C= 100 # Fitness function; high value means higher fitnessfitness= function(x){  sum(x == target)} # Mutate functionmutate= function(x, rate= 0.01){  idx= which(runif(length(target)) <= rate)  x[idx]= replicate(n= length(idx), expr= sample(x= chars, size= 1, replace= T))  x} # Evolve functionevolve= function(x){  parents= rep(list(x), C+1) # Repliction  parents[1:C]= lapply(parents[1:C], function(x) mutate(x)) # Mutation  idx= which.max(lapply(parents, function(x) fitness(x))) # Selection  parents[[idx]]} # Initialize first parentparent= sample(x= chars, size= length(target), replace= T) # Main programwhile (fitness(parent) < fitness(target)) {  parent= evolve(parent)  cat(paste0(parent, collapse=""), "\n")}`

output:

```YEHWROTERTMEZGMZ DMPYD ZCNKY
...
METHINKS IT IS LIKE A WEASEL
```

## Racket

` #lang racket (define alphabet " ABCDEFGHIJKLMNOPQRSTUVWXYZ")(define (randch) (string-ref alphabet (random 27))) (define (fitness s1 s2)  (for/sum ([c1 (in-string s1)] [c2 (in-string s2)])    (if (eq? c1 c2) 1 0))) (define (mutate s P)  (define r (string-copy s))  (for ([i (in-range (string-length r))] #:when (<= (random) P))    (string-set! r i (randch)))  r) (define (evolution target C P)  (let loop ([parent (mutate target 1.0)] [n 0])    ;; (printf "~a: ~a\n" n parent)    (if (equal? parent target)      n      (let cloop ([children (for/list ([i (in-range C)]) (mutate parent P))]                  [best #f] [fit -1])        (if (null? children)          (loop best (add1 n))          (let ([f (fitness target (car children))])            (if (> f fit)              (cloop (cdr children) (car children) f)              (cloop (cdr children) best fit)))))))) ;; Some random experiment using all of this(define (try-run C P)  (define ns    (for/list ([i 10])      (evolution "METHINKS IT IS LIKE A WEASEL" C P)))  (printf "~s Average generation: ~s\n" C (/ (apply + 0.0 ns) (length ns)))  (printf "~s      Total strings: ~s\n" C (for/sum ([n ns]) (* n 50))))(for ([C (in-range 10 501 10)]) (try-run C 0.001)) `

## REXX

### optimized

This REXX version:

•   allows random seed for repeatability of runs
•   allows mutation rate to be expressed as a percentage (%)
•   echoes specification(s) and target string
•   columnar alignment of output
•   optimized for speed (only one random number/mutation)
•   supports an alphabet with lowercase letters and other letters and/or punctuation.
`/*REXX program  demonstrates  an  evolutionary algorithm  (by using mutation).          */parse arg  children  MR  seed .                  /*get optional arguments from the C.L. */if children==''  then children = 10              /*# children produced each generation. */if MR      ==''  then MR       = "4%"            /*the character Mutation Rate each gen.*/if right(MR,1)=='%'  then MR=strip(MR,,"%")/100  /*expressed as a percent?  Then adjust.*/if seed\=='' then call random ,,seed             /*SEED allow the runs to be repeatable.*/abc   = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '  ;     Labc=length(abc)target= 'METHINKS IT IS LIKE A WEASEL' ;     Ltar=length(target)parent= mutate( left('',Ltar), 1)                /*gen rand string,same length as target*/say center('target string', Ltar, "─")   'children'        "mutationRate"say target  center(children,8)    center((MR*100/1)'%', 12);                  saysay center('new string'    ,Ltar, "─")   "closeness"       'generation'        do gen=0  until  parent==target;                     close=fitness(parent)       almost=parent                        do  children;                       child=mutate(parent,MR)                        _=fitness(child);                   if _<=close  then iterate                        close=_;                            almost=child                        say almost  right(close, 9)   right(gen,10)                        end   /*children*/       parent=almost       end   /*gen*/exit                                             /*stick a fork in it,  we're all done. *//*──────────────────────────────────────────────────────────────────────────────────────*/fitness: parse arg x; \$=0;   do k=1 for Ltar; \$=\$+(substr(x,k,1)==substr(target,k,1)); end         return \$/*──────────────────────────────────────────────────────────────────────────────────────*/mutate:  parse arg x,rate;  \$=                   /*set  X  to 1st argument, RATE to 2nd.*/                   do j=1  for Ltar;       r=random(1,100000)    /*REXX's max for RANDOM*/                   if .00001*r<=rate  then \$=\$ || substr(abc,r//Labc+1, 1)                                      else \$=\$ || substr(x  ,j        , 1)                   end   /*j*/         return \$`

output   when using the following input:   20   4%   11

```───────target string──────── children mutationRate
METHINKS IT IS LIKE A WEASEL    20         4%

─────────new string───────── closeness generation
TWLPLGNVVMXFBUKHUPEQXOCUPIUS         1          0
TWLPLGNVVMXFBU HUPEQXOCUPIUS         2          1
TWLPLGNVVMX BU HUPEQXOCUPIUS         3          2
TWLPLCNVFMX BP HUPEQAOCUPIUS         4          4
TWLPLQNVFMX BP HUPEQAOCUPGUL         5          6
TWLHLQNVFMX BS HUPEQAOUUPGUL         7          9
RWLHLQNZFMX BS HUPEQAOUUEGEL         8         14
RWLHLQNZFIX BS HUPEQAOUUEGEL         9         15
RWLHLQNZFIX BS HUPE AOUUEGEL        10         19
RWLHLQNZFIX BS LWPE AOUUEGEL        11         22
RWLHLQNZFIX BS LWPE A UUEGEL        12         28
RWLHLNNZFIX BS LWPE A UUEGEL        13         36
RELHLNNZFIX BE LWPE A UUAGEL        14         40
RELHLNNZFIX BE LWPE A UUASEL        15         43
RELHLNNZFIX BE LWKE A  UASEL        16         50
RELHLNNZFIT BE LWKE A  UASEL        17         62
RELHLNNSFIT IE LWKE A  UASEL        19         67
RETHLNNSFIT IE LWKE A  UASEL        20         71
RETHLNNSFIT IE LIKE A  UASEL        21         79
METHLNNSFIT IE LIKE A  LASEL        22         91
METHLNNSFIT IE LIKE A WLASEL        23        112
METHLNNSFIT IE LIKE A WEASEL        24        144
METHLNNS IT IE LIKE A WEASEL        25        151
METHLNKS IT IM LIKE A WEASEL        26        160
METHLNKS IT IS LIKE A WEASEL        27        164
METHINKS IT IS LIKE A WEASEL        28        170
```

### optimized, stemmed arrays

This REXX version uses stemmed arrays for the character-by-character comparison   [T.n]   as well as
generating a random character   [@.n]   during mutation,   thus making it slightly faster,   especially for a
longer string and/or a low mutation rate.

`/*REXX program  demonstrates  an  evolutionary algorithm  (by using mutation).          */parse arg  children  MR  seed .                  /*get optional arguments from the C.L. */if children==''  then children = 10              /*# children produced each generation. */if MR      ==''  then MR       = "4%"            /*the character Mutation Rate each gen.*/if right(MR,1)=='%'  then MR=strip(MR,,"%")/100  /*expressed as a percent?  Then adjust.*/if seed\==''  then call random ,,seed            /*SEED allow the runs to be repeatable.*/abc = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ ';     Labc=length(abc)        do i=0  for Labc                          /*define array  (for faster compare),  */       @.i=substr(abc, i+1, 1)                   /*     it's better than picking out a  */       end   /*i*/                               /*     byte from a character string.   */ target= 'METHINKS IT IS LIKE A WEASEL' ;    Ltar=length(target)        do i=1  for Ltar                          /*define an array (for faster compare),*/       T.i=substr(target, i, 1)                  /*     it's better than a byte-by-byte */       end   /*i*/                               /*     compare using character strings.*/ parent= mutate( left('', Ltar), 1)               /*gen rand string, same length as tar. */say center('target string', Ltar, "─")    'children'       "mutationRate"say target  center(children, 8)   center((MR*100/1)'%',12);                     saysay center('new string'   , Ltar, "─")    'closeness'      "generation"        do gen=0  until  parent==target;                     close=fitness(parent)       almost=parent                         do  children;                      child=mutate(parent,MR)                         _=fitness(child);                  if _<=close  then iterate                         close=_;                           almost=child                         say almost  right(close, 9)  right(gen, 10)                         end   /*children*/       parent=almost       end   /*gen*/exit                                             /*stick a fork in it,  we're all done. *//*──────────────────────────────────────────────────────────────────────────────────────*/fitness: parse arg x; \$=0;   do k=1  for Ltar;  \$=\$+(substr(x,k,1)==T.k);  end;   return \$/*──────────────────────────────────────────────────────────────────────────────────────*/mutate:  parse arg x,rate                        /*set  X  to 1st argument, RATE to 2nd.*/         \$=;       do j=1  for Ltar;       r=random(1, 100000)   /*REXX's max for RANDOM*/                   if .00001*r<=rate  then do;    _=r//Labc;    \$=\$ || @._;  end                                      else \$=\$ || substr(x, j, 1)                   end   /*j*/         return \$`

output   is the same as the previous version.

## Ring

` # Project : Evolutionary algorithm target = "METHINKS IT IS LIKE A WEASEL"parent = "IU RFSGJABGOLYWF XSMFXNIABKT"num = 0mutationrate = 0.5children = len(target) child = list(children)while parent != target        bestfitness = 0        bestindex = 0        for index = 1 to children             child[index] = mutate(parent, mutationrate)             fitness = fitness(target, child[index])             if fitness > bestfitness                 bestfitness = fitness                bestindex = index             ok        next         if bestindex > 0           parent = child[bestindex]           num = num + 1           see "" + num + ": " + parent + nl        okend  func fitness(text, ref)       f = 0       for i = 1 to len(text)            if substr(text, i, 1) = substr(ref, i, 1)               f = f + 1            ok       next       return (f / len(text)) func mutate(text, rate)        rnd = randomf()        if rate > rnd           c = 63+random(27)           if c = 64              c = 32           ok           rnd2 = random(len(text))           if rnd2 > 0              text[rnd2] = char(c)           ok        ok        return text func randomf()       decimals(10)       str = "0."       for i = 1 to 10            nr = random(9)            str = str + string(nr)       next       return number(str) `

Output:

```1: IU RFPGJABGOLYWF XSMFXNIABKT
2: IU RFPGJABGOLQWF XSMFXNIABKT
3: IU RFPGJABGOLQWF XSMAXNIABKT
4: IU RFPGJABGOLQWF XSMA NIABKT
5: IU RFPGJABGOLQWF XSMA NIABKT
6: IU RFPGJABGOLSWF XSMA NIABKT
7: IU RFPGJABGOLSWF XSMA NIABKT
8: IUTRFPGJABGOLSWF XSMA NIABKT
9: IUTRFPGSABGOLSWF XSMA NIABKT
10: IUTRFPGSABGOLSWF XSMA NIABKT
11: IUTRFPGSABGOLSWF XSMA NIABKT
12: IUTRFPGSABGOLSWF XSMA NIABKT
13: IUTRFPGSABGOLSWF XSMA NIABKE
14: IUTRFPGSABGOMSWF XSMA NIABKE
15: IUTRFPGSYBGOMSWF XSMA NIABKE
16: IUTRFPGSYBGOMSWF XSMA NIABKE
17: IUTRFPGSYBGOMSWF XSMA NIABKE
39: MUTRFPKSYIX MS LIXSMA NEASEL
40: MUTRFPKSYIX MS LIXYMA NEASEL
41: MUTRFPKS IX MS LIXYMA NEASEL
42: MUTRFPKS IX MS LIXYMA NEASEL
43: MUTRFPKS IX MS LIXYMA WEASEL
44: MUTRFPKS IX MS LIXYMA WEASEL
45: MUTRFPKS IX MS LIXYMA WEASEL
46: MUTRFPKS IJ MS LIXYMA WEASEL
47: MUTRFPKS IJ MS LIXYMA WEASEL
48: MUTRFPKS IJ MS LIXYMA WEASEL
49: MUTRFPKS IJ MS LIXYMA WEASEL
50: MUTRFPKS IJ MS LIXYMA WEASEL
51: MUTRFPKS IJ MS LIXYMA WEASEL
52: MUTRIPKS IJ MS LIXYMA WEASEL
53: MUTRIPKS IJ MS LIXYMA WEASEL
54: MUTRIPKS IJ MS LIXYMA WEASEL
55: MUTRIPKS IJ MS LIXYMA WEASEL
56: MUTRIPKS IJ MS LIKYMA WEASEL
57: MUTRIPKS IJ MS LIKYMA WEASEL
58: MUTRIPKS IJ MS LIKYMA WEASEL
59: MUTRIPKS IJ MS LIKYMA WEASEL
60: MUTRIPKS IJ MS LIKY A WEASEL
61: MUTRIPKS IJ MS LIKY A WEASEL
62: MUTRIPKS IJ MS LIKY A WEASEL
63: MUTRIPKS IT MS LIKY A WEASEL
64: MUTRIPKS IT MS LIKY A WEASEL
65: MUTRIPKS IT MS LIKY A WEASEL
66: MUTRIPKS IT MS LIKY A WEASEL
67: MUTRIPKS IT MS LIKE A WEASEL
68: MUTRIPKS IT MS LIKE A WEASEL
69: MUTRIPKS IT MS LIKE A WEASEL
70: MUTRIPKS IT MS LIKE A WEASEL
71: MUTVIPKS IT MS LIKE A WEASEL
72: MUTVIPKS IT MS LIKE A WEASEL
73: MUTVIPKS IT MS LIKE A WEASEL
74: MUTVIPKS IT MS LIKE A WEASEL
75: MUTVIPKS IT MS LIKE A WEASEL
76: MUTVIPKS IT MS LIKE A WEASEL
77: MUTVIPKS IT MS LIKE A WEASEL
78: METVIPKS IT MS LIKE A WEASEL
79: METVIPKS IT MS LIKE A WEASEL
80: METVIPKS IT RS LIKE A WEASEL
81: METVIPKS IT RS LIKE A WEASEL
82: METVIPKS IT RS LIKE A WEASEL
83: METFIPKS IT RS LIKE A WEASEL
84: METFIPKS IT RS LIKE A WEASEL
85: METFIPKS IT RS LIKE A WEASEL
86: METFIPKS IT RS LIKE A WEASEL
87: METFIPKS IT RS LIKE A WEASEL
88: METFIPKS IT RS LIKE A WEASEL
89: METHIPKS IT RS LIKE A WEASEL
90: METHINKS IT RS LIKE A WEASEL
91: METHINKS IT ?S LIKE A WEASEL
92: METHINKS IT ?S LIKE A WEASEL
93: METHINKS IT ?S LIKE A WEASEL
94: METHINKS IT ?S LIKE A WEASEL
95: METHINKS IT ?S LIKE A WEASEL
96: METHINKS IT ?S LIKE A WEASEL
97: METHINKS IT ?S LIKE A WEASEL
98: METHINKS IT ?S LIKE A WEASEL
99: METHINKS IT ?S LIKE A WEASEL
100: METHINKS IT ?S LIKE A WEASEL
101: METHINKS IT ?S LIKE A WEASEL
102: METHINKS IT ?S LIKE A WEASEL
103: METHINKS IT ?S LIKE A WEASEL
104: METHINKS IT ?S LIKE A WEASEL
105: METHINKS IT ?S LIKE A WEASEL
106: METHINKS IT ?S LIKE A WEASEL
107: METHINKS IT ?S LIKE A WEASEL
108: METHINKS IT ?S LIKE A WEASEL
109: METHINKS IT ?S LIKE A WEASEL
110: METHINKS IT ?S LIKE A WEASEL
111: METHINKS IT ?S LIKE A WEASEL
112: METHINKS IT ?S LIKE A WEASEL
113: METHINKS IT ?S LIKE A WEASEL
114: METHINKS IT ?S LIKE A WEASEL
115: METHINKS IT ?S LIKE A WEASEL
116: METHINKS IT ?S LIKE A WEASEL
117: METHINKS IT ?S LIKE A WEASEL
118: METHINKS IT ?S LIKE A WEASEL
119: METHINKS IT ?S LIKE A WEASEL
120: METHINKS IT ?S LIKE A WEASEL
121: METHINKS IT ?S LIKE A WEASEL
122: METHINKS IT ?S LIKE A WEASEL
123: METHINKS IT ?S LIKE A WEASEL
124: METHINKS IT ?S LIKE A WEASEL
125: METHINKS IT ?S LIKE A WEASEL
126: METHINKS IT ?S LIKE A WEASEL
127: METHINKS IT ?S LIKE A WEASEL
128: METHINKS IT IS LIKE A WEASEL
```

## Ruby

Works with: Ruby version 1.9.3+
for the `sample` method.
Translation of: C
`@target = "METHINKS IT IS LIKE A WEASEL"Charset = [" ", *"A".."Z"]COPIES = 100 def random_char; Charset.sample end def fitness(candidate)  sum = 0  candidate.chars.zip(@target.chars) {|x,y| sum += (x[0].ord - y[0].ord).abs}  100.0 * Math.exp(Float(sum) / -10.0)end def mutation_rate(candidate)  1.0 - Math.exp( -(100.0 - fitness(candidate)) / 400.0)end def mutate(parent, rate)  parent.each_char.collect {|ch| rand <= rate ? random_char : ch}.joinend def log(iteration, rate, parent)  puts "%4d %.2f %5.1f %s" % [iteration, rate, fitness(parent), parent]end iteration = 0parent = Array.new(@target.length) {random_char}.joinprev = "" while parent != @target  iteration += 1  rate = mutation_rate(parent)  if prev != parent    log(iteration, rate, parent)    prev = parent  end  copies = [parent] + Array.new(COPIES) {mutate(parent, rate)}  parent = copies.max_by {|c| fitness(c)}endlog(iteration, rate, parent)`
Output:
```   1 0.22   0.0 FBNLRACAYQJAAJRNKNGZJMBQWBBW
2 0.22   0.0 QBNLGHPAYQJALJZGZNGAJMVQLBBW
3 0.22   0.0 JBNLGDPA QJALJZOZNGGTMVKLTBV
4 0.22   0.0 NSNLGDPA QTAMJ OZNVGTMVHOTBV
5 0.22   0.0 NSNLGVPA QTAMR OZVVGT VHOTBV
6 0.22   0.0 NSWLGVPA QTAMR OZVHGD VHOTBV
7 0.22   0.0 NSWLGVPA QTALR OGJHGD VHOTBV
8 0.22   0.0 NSWLGNPA QTALR OGJHGE VHNTBV
9 0.22   0.0 NSWWGMPY QT LR OJAHGE VHNTBV
10 0.22   0.0 NSWWGMPW QT LR OJAH E VJNTXV
11 0.22   0.0 JSZWGMPW QT LR OQAH E VJNWLF
12 0.22   0.0 JJZGJMPW QT LR OIAH E VJNWLF
13 0.22   0.0 IJZGJMPW DT HR OIHH E VJNWLF
14 0.22   0.1 NJZGJMPW DT HR OIHH E VCEZLF
17 0.22   0.2 NJZGJMPW KT HR OIHH E VCEPLF
22 0.22   0.2 NDZGJMPQ KW HR OIHH E VCEPLF
25 0.22   0.3 NDZGJMPQ KW HR LIHH E VCEPOO
26 0.22   0.5 NDZGJQJQ JS HR LIHH E VCEPOO
28 0.22   0.6 NDZGJQJQ IS HR LIHH E VCEPOO
29 0.22   0.6 NDZGJLJQ IS HR LIHH E VCEPOO
30 0.22   0.7 NDZGJLJQ IS ER LIHH E VCEPKO
35 0.22   0.8 NDZGJLJQ IS KR LIHH E VCEPKO
40 0.22   1.5 NDZGJLJQ IS KR LINH D VCEPFO
46 0.22   1.7 NDZGJLJQ IS KR LIMH D VCEPFO
47 0.21   3.3 NDZGJLJQ IS KR LILB D VCAPFM
66 0.21   3.7 NDSGJLJQ IS KR LIGI D VCAPFM
67 0.21   4.5 NDSGJLJQ IS IR LIGI D VCAPFM
70 0.21   6.1 NDTGJLMQ IS IS LIGI D VCATFM
72 0.21   6.7 NDTGJLMQ IS IS LIHI D VCATFM
77 0.21   8.2 NDTGJLMQ IU IS LIHI B VCATFM
83 0.20   9.1 NDTGJLLQ IU IS LIHI B VCATFM
87 0.20  10.0 NDTGJLLQ IU IS LIHH B VCATFM
108 0.20  11.1 NDTGJLLT IU IS LIHH B VCATFM
118 0.19  13.5 NDTGJNLT IU IS LIHH B VCATFM
128 0.18  18.3 MDTGJNLT IU IS LILH B VCATFM
153 0.18  20.2 NDTGJNLT IU IS LILH B VEATFM
155 0.17  24.7 NDTGJNLT IU IS LILE B VDATFM
192 0.17  27.3 NDTGJNLS IU IS LILE B VDATFM
225 0.16  30.1 NDTGJNLS IU IS LILE B VDASFM
226 0.15  33.3 NDTGJNLS IU IS LILE B VDASFL
227 0.15  36.8 NDTGJNLS IT IS LILE B VDASFL
246 0.14  40.7 NDTGJNKS IT IS LILE B VDASFL
252 0.13  44.9 NETGJNKS IT IS LILE B VDASFL
256 0.12  49.7 NETGJNKS IT IS LILE B WDASFL
260 0.11  54.9 NETGINKS IT IS LILE B WDASDL
284 0.09  60.7 NETHINKS IT IS LILE B WDASDL
300 0.08  67.0 NETHINKS IT IS LIKE B WDASDL
309 0.06  74.1 NETHINKS IT IS LIKE B WDASEL
311 0.04  81.9 NETHINKS IT IS LIKE A WDASEL
316 0.02  90.5 METHINKS IT IS LIKE A WDASEL
335 0.02 100.0 METHINKS IT IS LIKE A WEASEL```

## Rust

`//! Author : Thibault Barbie//!//! A simple evolutionary algorithm written in Rust. extern crate rand; use rand::Rng; fn main() {    let target = "METHINKS IT IS LIKE A WEASEL";    let copies = 100;    let mutation_rate = 20; // 1/20 = 0.05 = 5%     let mut rng = rand::weak_rng();     // Generate first sentence, mutating each character    let start = mutate(&mut rng, target, 1); // 1/1 = 1 = 100%     println!("{}", target);    println!("{}", start);     evolve(&mut rng, target, start, copies, mutation_rate);} /// Evolution algorithm////// Evolves `parent` to match `target`.  Returns the number of evolutions performed.fn evolve<R: Rng>(    rng: &mut R,    target: &str,    mut parent: String,    copies: usize,    mutation_rate: u32,) -> usize {    let mut counter = 0;    let mut parent_fitness = target.len() + 1;     loop {        counter += 1;         let (best_fitness, best_sentence) = (0..copies)            .map(|_| {                // Copy and mutate a new sentence.                let sentence = mutate(rng, &parent, mutation_rate);                // Find the fitness of the new mutation                (fitness(target, &sentence), sentence)            })            .min_by_key(|&(f, _)| f) // find the closest mutation to the target            .unwrap(); // fails if `copies == 0`         // If the best mutation of this generation is better than `parent` then "the fittest        // survives" and the next parent becomes the best of this generation.        if best_fitness < parent_fitness {            parent = best_sentence;            parent_fitness = best_fitness;            println!(                "{} : generation {} with fitness {}",                parent, counter, best_fitness            );             if best_fitness == 0 {                return counter;            }        }    }} /// Computes the fitness of a sentence against a target string, returning the number of/// incorrect characters.fn fitness(target: &str, sentence: &str) -> usize {    sentence        .chars()        .zip(target.chars())        .filter(|&(c1, c2)| c1 != c2)        .count()} /// Mutation algorithm.////// It mutates each character of a string, according to a `mutation_rate`.fn mutate<R: Rng>(rng: &mut R, sentence: &str, mutation_rate: u32) -> String {    let maybe_mutate = |c| {        if rng.gen_weighted_bool(mutation_rate) {            random_char(rng)        } else {            c        }    };    sentence.chars().map(maybe_mutate).collect()} /// Generates a random letter or space.fn random_char<R: Rng>(rng: &mut R) -> char {    // Returns a value in the range [A, Z] + an extra slot for the space character.  (The `u8`    // values could be cast to larger integers for a better chance of the RNG hitting the proper    // range).    match rng.gen_range(b'A', b'Z' + 2) {        c if c == b'Z' + 1 => ' ', // the `char` after 'Z'        c => c as char,    }}`
Output:
```METHINKS IT IS LIKE A WEASEL
ZPNUDZUKIHR SRD SZNRWOZDAXJX
ZPNUDZKKIHR SRD SZNJWOZDAXJX : generation 1 with fitness 25
ZPHUDZKKIHR SRD SZNJWOWDAXJX : generation 2 with fitness 24
ZPGUDZKSIHR SRD SZNJWOWDAXJX : generation 3 with fitness 23
ZPGUDZKSIIR SRD SUNJWOWXAXJX : generation 4 with fitness 22
ZEGUDZKSIIR SRD SUNJWOWXAXJX : generation 5 with fitness 21
ZECUDZKSIIR SRD SUN WOWEAXJX : generation 6 with fitness 19
ZECUDZKSIIN SRD SUN AOWEAXJX : generation 7 with fitness 18
ZECUDSKSIIN IRD SUN AOWEAXJX : generation 8 with fitness 17
ZECUDSKSIIN IRDLSUN AOWEAXJX : generation 9 with fitness 16
ZETUDSKSIIN IRDLSUN AOWEAXJX : generation 10 with fitness 15
ZETUDSKSIIN IRDLSUN A WEAXJX : generation 11 with fitness 14
ZETUDSKSIIT IRDLSUN A WEAXJX : generation 12 with fitness 13
ZETUDSKSIIT IRDLSUN A WEAXER : generation 13 with fitness 12
ZETHDSKSIIT IRDLSUN A WEAXER : generation 14 with fitness 11
ZETHDSKSIIT IRDLSKN A WEAXER : generation 15 with fitness 10
ZETHDSKSIIT IRDLIKN A WEAXER : generation 17 with fitness 9
ZETHDSKSIIT IR LIKN A WEAXER : generation 19 with fitness 8
ZETHDSKS IT IR LIKN A WEAXER : generation 23 with fitness 7
ZETHDSKS IT IR LIKN A WEASER : generation 26 with fitness 6
ZETHDOKS IT IR LIKE A WEASER : generation 28 with fitness 5
ZETHDNKS IT IR LIKE A WEASER : generation 31 with fitness 4
ZETHCNKS IT IR LIKE A WEASEL : generation 45 with fitness 3
ZETHCNKS IT IS LIKE A WEASEL : generation 46 with fitness 2
METHCNKS IT IS LIKE A WEASEL : generation 68 with fitness 1
METHINKS IT IS LIKE A WEASEL : generation 79 with fitness 0
```

## Scala

Works with: Scala version 2.8.1
`import scala.annotation.tailrec case class LearnerParams(target:String,rate:Double,C:Int) val chars =  ('A' to 'Z') ++ List(' ')val randgen = new scala.util.Randomdef randchar = {   val charnum = randgen.nextInt(chars.size)   chars(charnum)} class RichTraversable[T](t: Traversable[T]) {    def maxBy[B](fn: T => B)(implicit ord: Ordering[B]) = t.max(ord on fn)    def minBy[B](fn: T => B)(implicit ord: Ordering[B]) = t.min(ord on fn)} implicit def toRichTraversable[T](t: Traversable[T]) = new RichTraversable(t) def fitness(candidate:String)(implicit params:LearnerParams) =   (candidate zip params.target).map { case (a,b) => if (a==b) 1 else 0 }.sum def mutate(initial:String)(implicit params:LearnerParams) =   initial.map{ samechar => if(randgen.nextDouble < params.rate) randchar else samechar } @tailrecdef evolve(generation:Int, initial:String)(implicit params:LearnerParams){   import params._   printf("Generation: %3d  %s\n",generation, initial)   if(initial == target) return ()   val candidates = for (number <- 1 to C) yield mutate(initial)   val next = candidates.maxBy(fitness)   evolve(generation+1,next)} implicit val params = LearnerParams("METHINKS IT IS LIKE A WEASEL",0.01,100)val initial = (1 to params.target.size) map(x => randchar) mkStringevolve(0,initial)`

## Scheme

` (import (scheme base)        (scheme write)        (srfi 27))     ; random numbers (random-source-randomize! default-random-source) (define target "METHINKS IT IS LIKE A WEASEL") ; target string(define C 100) ; size of population(define p 0.1) ; chance any char is mutated ;; return a random character in given range(define (random-char)  (string-ref "ABCDEFGHIJKLMNOPQRSTUVWXYZ "               (random-integer 27))) ;; compute distance of given string from target(define (fitness str)  (apply +         (map (lambda (c1 c2) (if (char=? c1 c2) 0 1))              (string->list str)              (string->list target)))) ;; mutate given parent string, returning a new string(define (mutate str)  (string-map (lambda (c)                 (if (< (random-real) p)                  (random-char)                  c))              str)) ;; create a population by mutating parent, ;; returning a list of variations(define (make-population parent)  (do ((pop '() (cons (mutate parent) pop)))    ((= C (length pop)) pop))) ;; find the most fit candidate in given list(define (find-best candidates)  (define (select-best a b)    (if (< (fitness a) (fitness b)) a b))  ;  (do ((best (car candidates) (select-best best (car rem)))       (rem (cdr candidates) (cdr rem)))    ((null? rem) best))) ;; create first parent from random characters;; of same size as target string(define (initial-parent)  (do ((res '() (cons (random-char) res)))    ((= (length res) (string-length target))     (list->string res)))) ;; run the search(do ((parent (initial-parent) (find-best (cons parent (make-population parent))))) ; select best from parent and population  ((string=? parent target)   (display (string-append "Found: " parent "\n")))  (display parent) (newline)) `

## Seed7

`\$ include "seed7_05.s7i"; const string: table is "ABCDEFGHIJKLMNOPQRSTUVWXYZ "; const func integer: unfitness (in string: a, in string: b) is func  result    var integer: sum is 0;  local    var integer: index is 0;  begin    for index range 1 to length(a) do      sum +:= ord(a[index] <> b[index]);    end for;  end func; const proc: mutate (in string: a, inout string: b) is func  local    var integer: index is 0;  begin    b := a;    for index range 1 to length(a) do      if rand(1, 15) = 1 then        b @:= [index] table[rand(1, 27)];      end if;    end for;  end func; const proc: main is func  local     const string: target is "METHINKS IT IS LIKE A WEASEL";     const integer: OFFSPRING is 30;     var integer: index is 0;     var integer: unfit is 0;     var integer: best is 0;     var integer: bestIndex is 0;     var integer: generation is 1;     var string: parent is " " mult length(target);     var array string: children is OFFSPRING times " " mult length(target);  begin    for index range 1 to length(target) do      parent @:= [index] table[rand(1, 27)];    end for;    repeat      for index range 1 to OFFSPRING do        mutate(parent, children[index]);      end for;      best := succ(length(parent));      bestIndex := 0;      for index range 1 to OFFSPRING do        unfit := unfitness(target, children[index]);        if unfit < best then          best := unfit;          bestIndex := index;        end if;      end for;       if bestIndex <> 0 then        parent := children[bestIndex];      end if;      writeln("generation " <& generation <& ": score " <& best <& ": " <& parent);      incr(generation);    until best = 0;  end func;`

## SequenceL

Translation of: C#

SequenceL Code:

`import <Utilities/Sequence.sl>; AllowedChars := " ABCDEFGHIJKLMNOPQRSTUVWXYZ"; initializeParent(randChars(1)) := AllowedChars[randChars]; Fitness(target(1), current(1)) :=	let		fit[i] := true when target[i] = current[i];	in		size(fit); Mutate(letter(0), rate(0), randRate(0), randChar(0)) := 		letter when randRate > rate	else		AllowedChars[randChar]; evolve(target(1), parent(1), C(0), P(0), rateRands(2), charRands(2)) :=	let		mutations[i] := Mutate(parent, P, rateRands[i], charRands[i]) foreach i within 1 ... C;		fitnesses := Fitness(target, mutations);	in		mutations[firstIndexOf(fitnesses, vectorMax(fitnesses))];`

C++ Driver Code:

`#include <iostream>#include <time.h>#include "SL_Generated.h" using namespace std; int main(int argc, char** argv){	int threads = 0; 	char* targetString = "METHINKS IT IS LIKE A WEASEL";	if(argc > 1) targetString = argv[1];	int C = 100;	if(argc > 2) C = atoi(argv[2]);	SL_FLOAT P = 0.05;	if(argc > 3) P = atof(argv[3]);	int seed = time(NULL);	if(argc > 4) seed = atoi(argv[4]); 	int targetDims[] = {strlen(targetString), 0};	Sequence<char> target((void*)targetString, targetDims); 	sl_init(threads); 	Sequence<char> parent;	Sequence<char> newParent;	Sequence<int> parentRands;	sl_create(seed++, 1, 27, target.size(), threads, parentRands);	sl_initializeParent(parentRands, threads, parent); 	Sequence< Sequence<int> > charRands;	Sequence< Sequence<SL_FLOAT> > rateRands; 	cout << "Start:\t" << parent << endl;	for(int i = 1; !(parent == target); i++)	{		sl_create(seed++, 1, 27, C, target.size(), threads, charRands);		sl_create(seed++, 0.0, 1.0, C, target.size(), threads, rateRands); 		sl_evolve(target, parent, C, P, rateRands, charRands, threads, newParent);		parent = newParent; 		cout << "#" << i << ":\t" << parent << endl;	}	cout << "End:\t" << parent << endl; 	sl_done(); 	return 0;}`
Output:
```Start:	"EDVSWRXSQWK VWUOGAWSTRJWY EW"
#1:	"EDVSWRXSQIK VWUOGAWSTRJWY EW"
#2:	"EDVSWRXSQIK VWUOGAESTRJWY EW"
#3:	"EDVSWRXSQIK VWUOGAESTRJWY EL"
#4:	"MDVSWRHSQIK VWUOGAESTRJWY EL"
#5:	"MDVSWRHSQIK VW OGAESTRJWY EL"
#6:	"MDVSWRHSQIK IW OGAESTRJOY EL"
#7:	"MDVSWRHSQIK IW OGAESTRWOY EL"
#8:	"MDVSWRHSQIK IW OGAESARWOY EL"
#9:	"MDVSWRHSQIK IW OGAESARWOY EL"
#10:	"MDVSWRHSQIK IW OGAESARWOY EL"
#11:	"MDVSWRHSQIK IW OGAESARWOY EL"
#12:	"MDVSWRHSQIK IW LGAESANWOY EL"
#13:	"MDVSWJHSXIK IW LGAESANWOY EL"
#14:	"MEVSWJHSXIK IW LGAESANWOY EL"
#15:	"MEVSWJHSXIK IA LVAESANWOY EL"
#16:	"MEVSWJHSXIK IA LVAESACWOY EL"
#21:	"MENIWRHS IK IA LVAE ACWOADEL"
#22:	"MENIWRHS IK IA LVAE A WOADEL"
#23:	"METIKRAS IK IA LCAE A WOADEL"
#24:	"METIKRAS IK IA LCIE A WOADEL"
#25:	"METIKRAS IK IA LCIE A WOASEL"
#26:	"METIKRAS IK IA LCIE A WOASEL"
#27:	"METIKRAS IK IA LCIE A WEASEL"
#28:	"METIKRKS IK IA LCIE A WEASEL"
#29:	"METIKRKS IK IA LCIE A WEASEL"
#30:	"METIKRKS IK IA LCIE A WEASEL"
#31:	"METIKRKS IK IU LOIE A WEASEL"
#32:	"METIKRKS IT IU LOIE A WEASEL"
#33:	"METIKRKS IT IU LOIE A WEASEL"
#34:	"METIKRKS IT IU LIIE A WEASEL"
#35:	"METIKRKS IT IU LIIE A WEASEL"
#36:	"METHKRKS IT IU LIIE A WEASEL"
#37:	"METHKRKS IT IU LIIE A WEASEL"
#38:	"METHKRKS IT IU LIIE A WEASEL"
#39:	"METHCRKS IT IU LIIE A WEASEL"
#40:	"METHCRKS IT IU LIIE A WEASEL"
#41:	"METHCRKS IT IU LIIE A WEASEL"
#42:	"METHCRKS IT IU LIIE A WEASEL"
#43:	"METHCRKS IT IU LIIE A WEASEL"
#44:	"METHCRKS IT IU LIIE A WEASEL"
#45:	"METHCRKS IT IU LIIE A WEASEL"
#46:	"METHZRKS IT IU LIKE A WEASEL"
#47:	"METHZRKS IT IU LIKE A WEASEL"
#48:	"METHZRKS IT IU LIKE A WEASEL"
#49:	"METHZRKS IT IU LIKE A WEASEL"
#50:	"METHGRKS IT IU LIKE A WEASEL"
#51:	"METHGRKS IT IL LIKE A WEASEL"
#52:	"METHGYKS IT IL LIKE A WEASEL"
#53:	"METHGYKS IT IL LIKE A WEASEL"
#54:	"METHIYKS IT IL LIKE A WEASEL"
#55:	"METHIYKS IT IS LIKE A WEASEL"
#56:	"METHIYKS IT IS LIKE A WEASEL"
#57:	"METHIYKS IT IS LIKE A WEASEL"
#58:	"METHIYKS IT IS LIKE A WEASEL"
#59:	"METHIYKS IT IS LIKE A WEASEL"
#60:	"METHIYKS IT IS LIKE A WEASEL"
#61:	"METHIYKS IT IS LIKE A WEASEL"
#62:	"METHIYKS IT IS LIKE A WEASEL"
#63:	"METHIYKS IT IS LIKE A WEASEL"
#64:	"METHIYKS IT IS LIKE A WEASEL"
#65:	"METHIYKS IT IS LIKE A WEASEL"
#66:	"METHIYKS IT IS LIKE A WEASEL"
#67:	"METHIYKS IT IS LIKE A WEASEL"
#68:	"METHIYKS IT IS LIKE A WEASEL"
#69:	"METHIYKS IT IS LIKE A WEASEL"
#70:	"METHIYKS IT IS LIKE A WEASEL"
#71:	"METHIYKS IT IS LIKE A WEASEL"
#72:	"METHIYKS IT IS LIKE A WEASEL"
#73:	"METHIYKS IT IS LIKE A WEASEL"
#74:	"METHIYKS IT IS LIKE A WEASEL"
#75:	"METHIYKS IT IS LIKE A WEASEL"
#76:	"METHIYKS IT IS LIKE A WEASEL"
#77:	"METHINKS IT IS LIKE A WEASEL"
End:	"METHINKS IT IS LIKE A WEASEL"
```

## Sidef

Translation of: Perl 6
`define target = "METHINKS IT IS LIKE A WEASEL"define mutate_chance = 0.08define alphabet = [('A'..'Z')..., ' ']define C = 100 func fitness(str) { str.chars ~Z== target.chars -> count(true) }func mutate(str)  { str.gsub(/(.)/, {|s1| 1.rand < mutate_chance ? alphabet.pick : s1 }) } for (    var (i, parent) = (0, alphabet.rand(target.len).join);    parent != target;    parent = C.of{ mutate(parent) }.max_by(fitness)) { printf("%6d: '%s'\n", i++, parent) }`

## Sinclair ZX81 BASIC

Requires at least 2k of RAM. Displaying everything while it's running (generation count, parent, children, and their fitness scores) slows it down somewhat; but it would be very slow anyway, and it's nice to be able to look in on it from time to time and see how the program's getting along.

` 10 LET A\$="ABCDEFGHIJKLMNOPQRSTUVWXYZ " 20 LET T\$="METHINKS IT IS LIKE A WEASEL" 30 LET L=LEN T\$ 40 LET C=10 50 LET M=0.05 60 LET G=0 70 DIM C\$(C,L) 80 LET P\$="" 90 FOR I=1 TO L100 LET P\$=P\$+A\$(INT (RND*LEN A\$)+1)110 NEXT I120 PRINT AT 1,0;P\$130 LET S\$=P\$140 GOSUB 390150 LET N=R160 PRINT AT 1,30;N170 PRINT AT 0,4;G180 IF P\$=T\$ THEN GOTO 440190 FOR I=1 TO C200 FOR J=1 TO L210 LET C\$(I,J)=P\$(J)220 IF RND<=M THEN LET C\$(I,J)=A\$(INT (RND*LEN A\$)+1)230 PRINT AT I+2,J-1;C\$(I,J)240 NEXT J250 PRINT AT I+2,30;"  "260 NEXT I270 LET F=0280 FOR I=1 TO C290 LET S\$=C\$(I)300 GOSUB 390310 PRINT AT I+2,30;R320 IF R>N THEN LET F=I330 IF R>N THEN LET N=R340 NEXT I350 IF F>0 THEN LET P\$=C\$(F)360 LET G=G+1370 PRINT AT 1,0;P\$380 GOTO 160390 LET R=0400 FOR K=1 TO L410 IF S\$(K)=T\$(K) THEN LET R=R+1420 NEXT K430 RETURN`
Output:
```    349
METHINKS IT IS LIKE A WEASEL  28

METHINKS ITCIS LIKE A WEASEL  27
METHINKS ITCIS LIKE A WEASEL  27
METHINKS ITCIS LIKECA WEASEL  26
METHINKS ITCIS LPKE AUWEASLL  24
METHINKS CTCIS LIKP A WEASEL  25
METHINKS ITCIC LIKE A WEASER  25
METHINKS I CIS LIKE A WEASEL  26
METHINKS IYCIS LIREAA WEAWEL  23
METHINKS IT IS LIKE A WEASEL  28
METHINKSIITCIS LIKE A WEASEJ  25```

## Smalltalk

Works with: GNU Smalltalk
`String subclass: Mutant [    <shape: #character>     Target := Mutant from: 'METHINKS IT IS LIKE A WEASEL'.    Letters := ' ABCDEFGHIJKLMNOPQRSTUVWXYZ'.     Mutant class >> run: c rate: p        ["Run Evolutionary algorighm, using c copies and mutate rate p."        | pool parent |        parent := self newRandom.        pool := Array new: c+1.         [parent displayNl.        parent = Target] whileFalse:            [1 to: c do: [:i | pool at: i put: (parent copy mutate: p)].            pool at: c+1 put: parent.            parent := pool fold: [:winner :each | winner fittest: each]]]     Mutant class >> newRandom        [^(self new: Target size)            initializeToRandom;            yourself]     initializeToRandom        [self keys do: [:i | self at: i put: self randomLetter]]     mutate: p        [self keys do:            [:i |            Random next <= p ifTrue: [self at: i put: self randomLetter]]]     fitness        [| score |        score := 0.        self with: Target do:            [:me :you |            me = you ifTrue: [score := score + 1]].        ^score]     fittest: aMutant        [^self fitness > aMutant fitness            ifTrue: [self]            ifFalse: [aMutant]]     randomLetter        [^Letters at: (Random between: 1 and: Letters size)]]`

Use example:

`st> Mutant run: 2500 rate: 0.1QJUUIQHYXEZORSXGJCAHEWACH KGQJUUIQHYXEZORSXGJCAHEWWCMSKGQEUUIUHYXEZORSOGICAHYWWCSSKGQETUIUHGXEZORS GICE YWWCSSEGMETUIUHSXOZORS OICE YWWCSSEGMETUIUHSXOZORS OICE Y WCSSEGMETUIUHSXOZMIS OIOE Y WCNSEGMETKIUKSTOFMIS LIOE Y WCNSEGMETKINKSTOFMIS LIKE E WCNSEGMETKINKSTOFMIS LIKE F WCNSELMETHINKSTOF IS LIKE F WCNSELMETHINKS OW IS LIKE F WCNSELMETHINKS IW IS LIKE F WCNSELMETHINKS IW IS LIKE C WCASELMETHINKS IW IS LIKE C WCASELMETHINKS IW IS LIKE A WCASELMETHINKS IW IS LIKE A WCASELMETHINKS IW IS LIKE A WEASELMETHINKS IT IS LIKE A WEASELMutant`

## Tcl

Works with: Tcl version 8.5
Translation of: Python
`package require Tcl 8.5 # A function to select a random character from an argument stringproc tcl::mathfunc::randchar s {    string index \$s [expr {int([string length \$s]*rand())}]} # Set up the initial variablesset target "METHINKS IT IS LIKE A WEASEL"set charset "ABCDEFGHIJKLMNOPQRSTUVWXYZ "set parent [subst [regsub -all . \$target {[expr {randchar(\$charset)}]}]]set MaxMutateRate 0.91set C 100 # Work with parent and target as lists of characters so iteration is more efficientset target [split \$target {}]set parent [split \$parent {}] # Generate the fitness *ratio*proc fitness s {    global target    set count 0    foreach c1 \$s c2 \$target {	if {\$c1 eq \$c2} {incr count}    }    return [expr {\$count/double([llength \$target])}]}# This generates the converse of the Python version; logically saner namingproc mutateRate {parent} {    expr {(1.0-[fitness \$parent]) * \$::MaxMutateRate}}proc mutate {rate} {    global charset parent    foreach c \$parent {	lappend result [expr {rand() <= \$rate ? randchar(\$charset) : \$c}]    }    return \$result}proc que {} {    global iterations parent    puts [format "#%-4i, fitness %4.1f%%, '%s'" \	    \$iterations [expr {[fitness \$parent]*100}] [join \$parent {}]]} while {\$parent ne \$target} {    set rate [mutateRate \$parent]    if {!([incr iterations] % 100)} que    set copies [list [list \$parent [fitness \$parent]]]    for {set i 0} {\$i < \$C} {incr i} {	lappend copies [list [set copy [mutate \$rate]] [fitness \$copy]]    }    set parent [lindex [lsort -real -decreasing -index 1 \$copies] 0 0]}puts ""que`

Produces this example output:

```#100 , fitness 42.9%, 'GSTBIGFS ITLSS LMD  NNJPESZL'
#200 , fitness 57.1%, 'SCTHIOAS ITHIS LNK  PPLEASOG'
#300 , fitness 64.3%, 'ILTHIBKS IT IS LNKE PPLEBSIS'
#400 , fitness 96.4%, 'METHINKS IT IS LIKE A  EASEL'

#431 , fitness 100.0%, 'METHINKS IT IS LIKE A WEASEL'```

Note that the effectiveness of the algorithm can be tuned by adjusting the mutation rate; with a Cadre size of 100, a very rapid convergence happens for a maximum mutation rate of 0.3…

### Alternate Presentation

This alternative presentation factors out all assumption of what constitutes a “fit” solution to the `fitness` command, which is itself just a binding of the `fitnessByEquality` procedure to a particular target. None of the rest of the code knows anything about what constitutes a solution (and only `mutate` and `fitness` really know much about the data being evolved).

`package require Tcl 8.5proc tcl::mathfunc::randchar {} {    # A function to select a random character    set charset "ABCDEFGHIJKLMNOPQRSTUVWXYZ "    string index \$charset [expr {int([string length \$charset] * rand())}]}set target "METHINKS IT IS LIKE A WEASEL"set initial [subst [regsub -all . \$target {[expr randchar()]}]]set MaxMutateRate 0.91set C 100 # A place-wise equality function defined over two lists (assumed equal length)proc fitnessByEquality {target s} {    set count 0    foreach c1 \$s c2 \$target {	if {\$c1 eq \$c2} {incr count}    }    return [expr {\$count / double([llength \$target])}]}# Generate the fitness *ratio* by place-wise equality with the target stringinterp alias  {} fitness  {} fitnessByEquality [split \$target {}] # This generates the converse of the Python version; logically saner namingproc mutationRate {individual} {    global MaxMutateRate    expr {(1.0-[fitness \$individual]) * \$MaxMutateRate}} # Mutate a string at a particular rate (per character)proc mutate {parent rate} {    foreach c \$parent {	lappend child [expr {rand() <= \$rate ? randchar() : \$c}]    }    return \$child} # Pretty printerproc prettyPrint {iterations parent} {    puts [format "#%-4i, fitness %5.1f%%, '%s'" \$iterations \	[expr {[fitness \$parent]*100}] [join \$parent {}]]} # The evolutionary algorithm itselfproc evolve {initialString} {    global C     # Work with the parent as a list; the operations are more efficient    set parent [split \$initialString {}]     for {set iterations 0} {[fitness \$parent] < 1} {incr iterations} {	set rate [mutationRate \$parent] 	if {\$iterations % 100 == 0} {	    prettyPrint \$iterations \$parent	} 	set copies [list [list \$parent [fitness \$parent]]]	for {set i 0} {\$i < \$C} {incr i} {	    lappend copies [list \		    [set copy [mutate \$parent \$rate]] [fitness \$copy]]	}	set parent [lindex [lsort -real -decreasing -index 1 \$copies] 0 0]    }    puts ""    prettyPrint \$iterations \$parent     return [join \$parent {}]} evolve \$initial`

## uBasic/4tH

This is a bit of a stretch, since uBasic/4tH doesn't support strings. Hence, the array is used to store the data.

`T = 0                                  ' Address of targetL = 28                                 ' Length of stringP = T + L                              ' Address of parentR = 6                                  ' Mutation rate in percentC = 7                                  ' Number of childrenB = 0                                  ' Best rate so far Proc _Initialize                       ' Initialize Do                                     ' Now start mutating  I = 0                                ' Nothing does it better so far   For x = 2 To C+1                     ' Addresses of children    Proc _MutateDNA (x, P, R)          ' Now mutate their DNA    F = FUNC(_Fitness (x, T))          ' Check for fitness    If F > B Then B = F : I = x        ' If fitness of child is better  Next                                 ' Make it the best score   If I Then                            ' If a better child was found    Proc _MakeParent (P, I)            ' Make the child the parent    Proc _PrintParent (P)              ' Print the new parent  EndIf   Until B = L                          ' Until top score equals lengthLoop End  _MutateDNA Param(3)                    ' Mutate an entire DNA  Local(1)   For [email protected] = 0 to L-1                    ' For the entire string    If [email protected] > Rnd(100) Then              ' If mutation rate is met       @([email protected]*[email protected]) = Ord("A") + Rnd(27) ' Mutate the gene    Else       @([email protected]*[email protected]) = @([email protected][email protected])           ' Otherwise copy it from the parent    EndIf  NextReturn  _Fitness Param(2)                      ' Check for fitness  Local(2)   [email protected] = 0                               ' Fitness is zero  For [email protected] = 0 to L-1                    ' For the entire string    If @([email protected]*[email protected]) = @([email protected][email protected]) Then [email protected] = [email protected] + 1  Next                                 ' If string matches, increment scoreReturn ([email protected])                            ' Return the fitness  _MakeParent Param(2)                   ' Make a child into a parent  Local(1)   For [email protected] = 0 to L-1                    ' For the entire string    @([email protected][email protected]) = @([email protected]*[email protected])              ' Copy the DNA gene by gene  NextReturn  _PrintParent Param(1)                  ' Print the parent  Local(1)   For [email protected] = 0 to L-1                    ' For the entire string    If (@([email protected][email protected])) > Ord ("Z") Then      Print " ";                       ' Cater for the space    Else      Print CHR(@([email protected][email protected]));             ' Print a gene    EndIf  Next   Print                                ' Issue a linefeedReturn  _Initialize                            ' Initialize target and parent  @(0)=Ord("M")                        ' Initialize target (long!)  @(1)=Ord("E")                        ' Character by character  @(2)=Ord("T")  @(3)=Ord("H")  @(4)=Ord("I")  @(5)=Ord("N")  @(6)=Ord("K")  @(7)=Ord("S")  @(8)=Ord("Z")+1  @(9)=Ord("I")  @(10)=Ord("T")  @(11)=Ord("Z")+1  @(12)=Ord("I")  @(13)=Ord("S")  @(14)=Ord("Z")+1  @(15)=Ord("L")  @(16)=Ord("I")  @(17)=Ord("K")  @(18)=Ord("E")  @(19)=Ord("Z")+1  @(20)=Ord("A")  @(21)=Ord("Z")+1  @(22)=Ord("W")  @(23)=Ord("E")  @(24)=Ord("A")  @(25)=Ord("S")  @(26)=Ord("E")  @(27)=Ord("L")   Proc _MutateDNA (P/L, P, 100)          ' Now mutate the parent DNAReturn`
Output:
```ZACXCLONTNTEAMJXYYFEP QQMDTA
ZACXILONTBTEALJXYYFEP QQPDTA
ZACNILONTBTEALJXYYXER WQPDTA
ZACNILKNTBTEALJXYYXER WQPDTA
ZACNILKNWBTEALJLYYXER WQPDTA
ZACNIEKNYITEALJLYYPER WSPDTA
ZACNIEKNYITEALJLYYPEA WSPDTA
QYCNIEKNYITEALJLYYPEA WSPDTL
MYCGIEKNYITEALJLYYPEA WSPDTL
MYCGIGKN ITEALJLYYPEA WSUDTL
MYCJIGKN ITEKLJLIYPEA WSUDTL
MYCJIGKN ITEKLJLIYP A WSUDTL
MYCJIGKN ITUKL LIYP A WSUDCL
MYCJIGKS ITUKL LIYP A WSRDCL
MYCJIGKS ITUUL LIYP A WSRDEL
MYCJIGKS ITUUL LIYP A WSRSEL
MYCJIGKS ITTUL LIYP A WWASEL
MECJIGKS ITTUL LIYP A WWASEL
MECHIGKS ITTUL LIYP A WWASEL
MECHIGKS ITTUS LIYP A WWASEL
MECHINKS ITTUS LIYP A WWASEL
MECHINKS ITOUS LIYE A WWASEL
MECHINKS ITOUS LIYE A WEASEL
MECHINKS ITOIS LIYE A WEASEL
MECHINKS ITOIS LIKE A WEASEL
MECHINKS IT IS LIKE A WEASEL
METHINKS IT IS LIKE A WEASEL

0 OK, 0:962```

## Ursala

The fitness function is given by the number of characters in the string not matching the target. (I.e., 0 corresponds to optimum fitness.) With characters mutated at a fixed probability of 10%, it takes about 500 iterations give or take 100.

`#import std#import nat rand_char = arc ' ABCDEFGHIJKLMNOPQRSTUVWXYZ' target = 'METHINKS IT IS LIKE A WEASEL' parent = rand_char* target fitness = length+ (filter ~=)+ zip/target mutate("string","rate") = "rate"%~?(rand_char,~&)* "string" C = 32 evolve = @iiX ~&l->r @r -*iota(C); @lS nleq\$-&l+ ^(fitness,~&)^*C/~&h mutate\*10 #cast %s main = evolve parent`

output:

```'METHINKS IT IS LIKE A WEASEL'
```

## UTFool

` ···http://rosettacode.org/wiki/Evolutionary_algorithm···■ Evolutionary  § static    target⦂ String: "METHINKS IT IS LIKE A WEASEL"    letter⦂ char[]: "ABCDEFGHIJKLMNOPQRSTUVWXYZ ".toCharArray°    parent⦂ String    random⦂ java.util.Random°    rate⦂ double: 0.5    C⦂ int: 1000     ▶ fittness⦂ int    · computes the 'closeness' of its    • argument⦂ String · to the target string      closeness⦂ int: 0      ∀ i ∈ 0 … target.length°        closeness◥ if target.charAt i = argument.charAt i      return closeness     ▶ mutate⦂ String · returns a copy of the    • given⦂ String  · with some characters probably mutated    • rate⦂ double      copy⦂ char[]: given.toCharArray°      ∀ i ∈ 0 … given.length°        copy[i]: letter[random.nextInt letter.length] if rate > random.nextDouble°      return String.valueOf copy     ▶ main    • args⦂ String[]      ancest⦂ StringBuilder°      ∀ i ∈ 0 … target.length°        ancest.append letter[random.nextInt letter.length]      parent: ancest.toString°      currentFittness⦂ int: fittness parent      generation⦂ int: 0      🔁 until the parent ≈ target        if fittness parent > currentFittness           currentFittness: fittness parent           System.out.println "Fittness of generation #⸨generation⸩ is ⸨currentFittness⸩"        for each time from 1 to C            mutation⦂ String: mutate parent, rate            parent: mutation if fittness parent < fittness mutation        generation◥      System.out.println "Target reached by generation #⸨generation⸩" `

## vbscript

` 'This is the string we want to "evolve" to. Any string of any length will'do as long as it consists only of upper case letters and spaces. Target  = "METHINKS IT IS LIKE A WEASEL" 'This is the pool of letters that will be selected at random for a mutation letters = " ABCDEFGHIJKLMNOPQRSTUVWXYZ" 'A mutation rate of 0.5 means that there is a 50% chance that one letter'will be mutated at random in the next child mutation_rate = 0.5 'Set for 10 children per generation Dim child(10) 'Generate the first guess as random letters RandomizeParent = "" for i = 1 to len(Target)    Parent = Parent & Mid(letters,Random(1,Len(letters)),1)next gen = 0 Do    bestfit = 0    bestind = 0     gen = gen + 1     'make n copies of the current string and find the one    'that best matches the target string     For i = 0 to ubound(child)         child(i) = Mutate(Parent, mutation_rate)         fit = Fitness(Target, child(i))         If fit > bestfit Then            bestfit = fit            bestind = i        End If     Next     'Select the child that has the best fit with the target string     Parent = child(bestind)    Wscript.Echo parent, "(fit=" & bestfit & ")" Loop Until Parent = Target Wscript.Echo vbcrlf & "Generations = " & gen 'apply a random mutation to a random character in a string Function Mutate ( ByVal str , ByVal rate )     Dim pos        'a random position in the string'    Dim ltr        'a new letter chosen at random    '     If rate > Rnd(1) Then         ltr = Mid(letters,Random(1,len(letters)),1)        pos = Random(1,len(str))        str = Left(str, pos - 1) & ltr & Mid(str, pos + 1)     End If     Mutate = str End Function 'returns the number of letters in the two strings that match Function Fitness (ByVal str , ByVal ref )     Dim i     Fitness = 0     For i = 1 To Len(str)        If Mid(str, i, 1) = Mid(ref, i, 1) Then Fitness = Fitness + 1    Next End Function 'Return a random integer in the range lower to upper (inclusive) Private Function Random ( lower , upper )  Random = Int((upper - lower + 1) * Rnd + lower)End Function`

Example output:

```JTXBMMYUFUWTKJRVVNOGGUAIGSIF (fit=1)
JTXBMMYUFYWTKJRVVNOGGUAIGSIF (fit=1)
JTXKMMYUFYWTKJRVVNOGGUAIGSIF (fit=1)
JTXKMMYUFYWTKJRVVNOGGUAIGSIF (fit=1)
UTXKMMYUFYWTKJRVVNOGGUAIGSIF (fit=1)
UTXKMMYUFYWTKJJVVNOGGUAIGSIF (fit=1)
UTXKMMYUFYWTKJJVVNDGGUAIGSIF (fit=1)
UTXKMMYUFYWTKJJVVNDGGUAIGSIF (fit=1)
UTXKMMYUFYWTKJJVVNDGGUWIGSIF (fit=2)
UTXKMMYUFYWTKJJVVNDGGUWIGSIF (fit=2)
UTXKMMYUFYWTKJJVVNDGGUWIGSIF (fit=2)
UBXKMMYUFYWTKJJVVNDGGUWIGSIF (fit=2)
UBNKMMYUFYWTKJJVVNDGGUWIGSIF (fit=2)
.
.
.
METHINKS IT IS LIKEVA WEASEL (fit=27)
METHINKS IT IS LIKEVA WEASEL (fit=27)
METHINKS IT IS LIKEVA WEASEL (fit=27)
METHINKS IT IS LIKEVA WEASEL (fit=27)
METHINKS IT IS LIKEVA WEASEL (fit=27)
METHINKS IT IS LIKE A WEASEL (fit=28)

Generations = 580
```

## Visual Basic

Adapted from BBC Basic Code in this page. One diference from BBC Basic code is that in this code mutations are always good

`   Option Explicit Private Sub Main()   Dim Target   Dim Parent   Dim mutation_rate   Dim children   Dim bestfitness   Dim bestindex   Dim Index   Dim fitness       Target = "METHINKS IT IS LIKE A WEASEL"      Parent = "IU RFSGJABGOLYWF XSMFXNIABKT"      mutation_rate = 0.5       children = 10      ReDim child(children)       Do        bestfitness = 0        bestindex = 0        For Index = 1 To children          child(Index) = FNmutate(Parent, mutation_rate, Target)          fitness = FNfitness(Target, child(Index))          If fitness > bestfitness Then            bestfitness = fitness            bestindex = Index          End If        Next Index         Parent = child(bestindex)        Debug.Print Parent      Loop Until Parent = Target      End  End Sub Function FNmutate(Text, Rate, ref)   Dim C As Integer   Dim Aux As Integer      If Rate > Rnd(1) Then        C = 63 + 27 * Rnd() + 1        If C = 64 Then C = 32        Aux = Len(Text) * Rnd() + 1        If Mid(Text, Aux, 1) <> Mid(ref, Aux, 1) Then            Text = Left(Text, Aux - 1) & Chr(C) & Mid(Text, Aux + 1)        End If      End If      FNmutate = TextEnd FunctionFunction FNfitness(Text, ref)    Dim I, F      For I = 1 To Len(Text)        If Mid(Text, I, 1) = Mid(ref, I, 1) Then F = F + 1      Next      FNfitness = F / Len(Text)End Function `

Example output:

```U RFSGJABGOLYWF XSMFXNIABKT
IU RFSGJABGOLYWF XSMFXNIABKT
IU NFSGJABGOLYWF XSMFXNIABKT
IU NFSGJABGOLYWF XSMFXNIABKT
IU NFSGJABGOLYWF XSMFXNIABOT
IUFNISGJABGOLYWF TSMFXCIABOT
IUFNISGJABGOLYWF TSMFXCIABOT
IUFNISGRABGOLYWF TSMFXCIABOT
.....
IEFMI GUASGLOYWF DSMFPRIAROT
IEFMI GUASGLOYWF DSMFPRZAROT
IEFMI GUASGLOYWFFDSMFPRZAROT
IEFMI GUASGLOYWFFDSMFPRZAQOT
IEFMI GUASGLOYBFFDSMFPRZAQOT
.....
METHINKS IT IS LVKE A WEASEL
METHINKS IT IS LVKE A WEASEL
METHINKS IT IS LRKE A WEASEL
METHINKS IT IS LRKE A WEASEL
METHINKS IT IS LRKE A WEASEL
METHINKS IT IS LRKE A WEASEL
METHINKS IT IS LIKE A WEASEL

```

## XPL0

`include c:\cxpl\codes;          \intrinsic code declarationsstring  0;                      \use zero-terminated convention (instead of MSb) def     MutateRate = 15,        \1 chance in 15 of a mutation        Copies = 30;            \number of mutated copieschar    Target, AlphaTbl;int     SizeOfAlpha;  func    StrLen(Str);    \Return the number of characters in a stringchar    Str;int     I;for I:= 0 to -1>>1-1 do        if Str(I) = 0 then return I;  func    Unfitness(A, B); \Return number of characters different between A and Bchar    A, B;int     I, C;[C:= 0;for I:= 0 to StrLen(A)-1 do        if A(I) # B(I) then C:= C+1;return C;];      \Unfitness  proc    Mutate(A, B);   \Copy string A to B, but with each character of B havingchar    A, B;           \ a 1 in MutateRate chance of differing from Aint     I;[for I:= 0 to StrLen(A)-1 do        B(I):= if Ran(MutateRate) then A(I) else AlphaTbl(Ran(SizeOfAlpha));B(I):= 0;               \terminate string];      \Mutate  int     I, BestI, Diffs, Best, Iter;def     SizeOfTarget = 28;char    Specimen(Copies, SizeOfTarget+1);int     ISpecimen, Temp; [Target:= "METHINKS IT IS LIKE A WEASEL";AlphaTbl:= "ABCDEFGHIJKLMNOPQRSTUVWXYZ ";SizeOfAlpha:= StrLen(AlphaTbl);ISpecimen:= Specimen;   \integer accesses pointers rather than bytes \Initialize first Specimen, the parent, to a random stringfor I:= 0 to SizeOfTarget-1 do        Specimen(0,I):= AlphaTbl(Ran(SizeOfAlpha));Specimen(0,I):= 0;      \terminate string Iter:= 0;repeat  for I:= 1 to Copies-1 do Mutate(ISpecimen(0), ISpecimen(I));         Best:= SizeOfTarget;            \find best matching string        for I:= 0 to Copies-1 do                [Diffs:= Unfitness(Target, ISpecimen(I));                if Diffs < Best then [Best:= Diffs;  BestI:= I];                ];        if BestI \#0\ then              \swap best string with first string                [Temp:= ISpecimen(0);                ISpecimen(0):= ISpecimen(BestI);                ISpecimen(BestI):= Temp;                ];        Text(0, "Iter ");  IntOut(0, Iter);        Text(0, " Score ");  IntOut(0, Best);        Text(0, ": ");  Text(0, ISpecimen(0));  CrLf(0);        Iter:= Iter+1;until   Best = 0;]`

Example output:

```Iter 0 Score 26: YIOHAVRGQLXRZJOSHNPRY VIQDNK
Iter 1 Score 25: YYOHAVRGQLX ZJOSHNPRY VIQDNK
Iter 2 Score 24: YYOHAVRGQLX ZJOSHNPRY VIQSNK
Iter 3 Score 24: YYOHAVRGQLX ZJOSHNPRY VIQSNK
Iter 4 Score 23: YYOHAVRGQLX ZJOSHNERY VIQSNK
Iter 5 Score 22: YYUHAVRGQLX ZJOSHNERY JDQSNL
...
Iter 200 Score 1: METHINKS IT IS LIKE K WEASEL
Iter 201 Score 1: METHINKS IT IS LIKE K WEASEL
Iter 202 Score 1: METHINKS IT IS LIKE K WEASEL
Iter 203 Score 0: METHINKS IT IS LIKE A WEASEL
```

## zkl

Translation of: D
`const target = "METHINKS IT IS LIKE A WEASEL";const C = 100;  // Number of children in each generation.const P = 0.05; // Mutation probability.const A2ZS = ["A".."Z"].walk().append(" ").concat();fcn fitness(s){ Utils.zipWith('!=,target,s).sum(0) } // bigger is worserfcn rnd{ A2ZS[(0).random(27)] }fcn mutate(s){ s.apply(fcn(c){ if((0.0).random(1) < P) rnd() else c }) } parent := target.len().pump(String,rnd);  // random string of "A..Z "gen:=0; do{  // mutate C copies of parent and pick the fittest   parent = (0).pump(C,List,T(Void,parent),mutate)	    .reduce(fcn(a,b){ if(fitness(a)<fitness(b)) a else b });   println("Gen %2d, dist=%2d: %s".fmt(gen+=1, fitness(parent), parent));}while(parent != target);`
Output:
```Gen  1, dist=26: JNGUIMCMOLLEULERIFPCYYZA  JR
Gen  2, dist=25: JNGUIMCMOLLEULERIFECYYZA  JR
Gen  3, dist=24: JNGUIMVMOLLEILERIFECYYZA  JU
...
Gen  7, dist=20: GNPHIMKMCLLEI ERIFECY ZA SJU
Gen  8, dist=19: GNPHIMKMCLLEI ERIKECY Z  SJH
...
Gen 13, dist=14: CNTHIMKSCLHEIB RIKECY ME S L
Gen 14, dist=14: CNTHIMKSCLHEIB RIKECY ME S L
Gen 15, dist=14: CNTHIMKSCLHEIB RIKECY ME S L
...
Gen 24, dist= 7: MLTHIMKS LTEIB MIKE Y WEASEL
Gen 25, dist= 7: MLTHIMKS LTEIB MIKE Y WEASEL
Gen 26, dist= 7: MLTHIMKS LTEIB KIKE Y WEASEL
...
Gen 48, dist= 1: METHINKS IT IS LIKE Z WEASEL
Gen 49, dist= 1: METHINKS IT IS LIKE G WEASEL
Gen 50, dist= 0: METHINKS IT IS LIKE A WEASEL
```