Evolutionary algorithm: Difference between revisions
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380: METHINKS ITBIS LIKE A WEASEL, fitness: 27
Final ( 384): METHINKS IT IS LIKE A WEASEL, fitness: 28</pre>
=={{header|ABC}}==
<syntaxhighlight lang="ABC">PUT "ABCDEFGHIJKLMNOPQRSTUVWXYZ " IN alphabet
HOW TO RETURN initial.state target:
SHARE alphabet
PUT "" IN state
FOR c IN target: PUT state^choice alphabet IN state
RETURN state
HOW TO RETURN state fitness target:
PUT #target IN score
FOR i IN {1..#target}:
IF state item i = target item i: PUT score-1 IN score
RETURN score
HOW TO RETURN chance mutate state:
SHARE alphabet
PUT "" IN mutated
FOR i IN {1..#state}:
SELECT:
random < chance: PUT choice alphabet IN next
ELSE: PUT state item i IN next
PUT mutated^next IN mutated
RETURN mutated
HOW TO EVOLVE TOWARD target:
PUT 0.1 IN mutation.rate
PUT 100 IN generation.size
PUT initial.state target IN state
WHILE state fitness target > 0:
WRITE (state fitness target)>>2, ": ", state/
PUT {} IN next.generation
FOR i IN {1..generation.size}:
PUT mutation.rate mutate state IN child
PUT child fitness target IN score
PUT child IN next.generation[score]
PUT next.generation[min keys next.generation] IN state
WRITE (state fitness target)>>2, ": ", state/
EVOLVE TOWARD "METHINKS IT IS LIKE A WEASEL"</syntaxhighlight>
{{out}}
<pre>27: CYPPYRQHMACPLQIZLPETRJVVPYLI
26: CYPPYRQHMICPLQIZLPEDRJVVPILI
25: CYKPYRQH ICPLWIZLPEDRJVJPILI
24: CWKPWWQH ICPLSIZLPEDRJVJPILI
23: CWKPWWQH ICPLSIZLPEDR BJPILI
21: CWKPWWQH ICPLSIZLSEDA BJA LI
20: JWKPWWKH ICPLSIZLSEDA BJA LK
19: WWKPIWKG ICPLSIZLSEDA BJA LK
18: WWKPIWKG ICPLSILLSEDA BJA LK
17: WWKPIWKG ICPLSILISEDA BJA LK
17: IWMPIWKG ICPLSILISEDA BJA LK
16: IWMPIWKG ICPLSILISEDA WJA LK
15: IWMPIWKG ICPLSILISEDA WEA LK
...
1: METHINKS IT IS LIKE A WEAS L
1: METHINKS IT IS LIKE A WEAS L
1: METHINKS IT IS LIKE A WEAS L
0: METHINKS IT IS LIKE A WEASEL</pre>
=={{header|Aime}}==
Line 2,648 ⟶ 2,708:
weasel()</syntaxhighlight>
=={{header|EasyLang}}==
<syntaxhighlight>
target$ = "METHINKS IT IS LIKE A WEASEL"
abc$[] = strchars " ABCDEFGHIJLKLMNOPQRSTUVWXYZ"
P = 0.05
C = 100
func fitness trial$ .
for i to len trial$
res += if substr trial$ i 1 <> substr target$ i 1
.
return res
.
func$ mutate parent$ .
for c$ in strchars parent$
if randomf < P
res$ &= abc$[randint len abc$[]]
else
res$ &= c$
.
.
return res$
.
for i to len target$
parent$ &= abc$[randint len abc$[]]
.
while fitness parent$ > 0
copies$[] = [ ]
for i to C
copies$[] &= mutate parent$
.
parent$ = copies$[1]
for s$ in copies$[]
if fitness s$ < fitness parent$
parent$ = s$
.
.
step += 1
print step & " " & parent$
.
</syntaxhighlight>
=={{header|EchoLisp}}==
Line 4,524 ⟶ 4,625:
=={{header|jq}}==
{{Works with|jq}}
'''Works with gojq, the Go implementation of jq(*)'''
'''Works with jaq, the Rust implementation of jq(*)'''
In this entry, the "fitness" score is based on codepoint differences;
Line 4,536 ⟶ 4,641:
< /dev/random tr -cd '0-9' | fold -w 3 | $JQ -cnr -f evolutionary-algorithm.jq
</pre>
(*) For gojq and jaq, leading 0s must be stripped from the input, e.g. by
`sed -e '/^0./s/0//' -e '/^0./s/0//'`.
<syntaxhighlight lang="jq">
# Assumption: input consists of random three-digit numbers i.e. 000 to 999
Line 4,564 ⟶ 4,673:
# Output: a mutation of . such that each character is mutated with probability $r
def mutate($r):
# Output: a pseudo-random character from set
# $n should be a random number drawn from range(0; N) inclusive where N > set|length
. as $p
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