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 thetarget
:
- copy the
parent
C times, each time allowing some random probability that another character might be substituted usingmutate
. - Assess the
fitness
of the parent and all the copies to thetarget
and make the most fit string the newparent
, discarding the others. - repeat until the parent converges, (hopefully), to the target.
- copy the
- See also
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 thetarget
:
- copy the
parent
C times, each time allowing some random probability that another character might be substituted usingmutate
.
- copy the
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 don't already match the target:
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
<lang forth> \ 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, #mutations 23 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 r@ 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</lang>
The output:
PIQSLOGHISTIPSDLZFGRDBYUCADA PIQSNOGH SQIPSDLZFGRDBYUEADA 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
Ada
Very simple fitness determination. For testing purposes you can add a static seed value to the RNG initializations (sample output uses '12345' for both).
<lang Ada>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 := "METHINKS@IT@IS@LIKE@A@WEASEL";
-- 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;</lang>
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
<lang aime>integer fitness(data t, data b) {
integer f, i;
f = 0;
i = b_length(t); while (i) { i -= 1; f += sign(t[i] ^ b[i]); }
return f;
}
void mutate(data c, data b, data u) {
integer i, l;
l = b_length(b); i = 0; while (i < l) { if (drand(15)) { b_append(c, b[i]); } else { b_append(c, u[drand(26)]); } i += 1; }
}
integer main(void) {
data b, t, u; integer f, i, l;
b_cast(t, "METHINK IT IS LIKE A WEASEL"); b_cast(u, "ABCDEFGHIJKLMNOPQRSTUVWXYZ ");
l = b_length(t);
i = l; while (i) { i -= 1; b_append(b, u[drand(26)]); }
f = fitness(t, b); while (f) { data n; integer a;
o_form("/lw4/~\n", f, b_string(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_string(b));
return 0;
}</lang>
- 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
Note: This specimen retains the original C coding style.
<lang algol68>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
)</lang> 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
<lang AutoHotkey>output := "" target := "METHINKS IT IS LIKE A WEASEL" targetLen := StrLen(target) Loop, 26 possibilities_%A_Index% := Chr(A_Index+64) ; A-Z possibilities_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 ", " iter MsgBox, % output ExitApp
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 }</lang> 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. <lang awk>
- !/bin/awk -f
function 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 = 0 for (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 = 0.10 if (ARGC > 1) mutrate = ARGV[1] lenset = length(charset) C = 100 D = C + 1 parent = "" for (j=1; j < length(target)+1; j++) {
parent = parent randchar() }
print "target: " target print "fitness of target: " fitness(target,target) print "initial parent: " parent gens = 0 while (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 }
</lang> 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
<lang dos> @echo off setlocal 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 L set 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=0 for %%i in (%target%) do (
set /a tempcount+=1 set target!tempcount!=%%i
) call:parent
echo %target% echo --------------------------------------------------------
- loop
call:fitness parent set currentfit=%errorlevel% if %currentfit%==28 goto end echo %parent% - %currentfit% [%attempts%] set attempts=0
- innerloop
set /a attempts+=1 title Attemps - %attempts% call:mutate %parent% call:fitness tempparent set 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
- end
echo %parent% - %currentfit% [%attempts%] echo Done. exit /b
- parent
set "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
- fitness
set fitness=0 set array=%1 for /l %%i in (1,1,28) do if !%array%%%i!==!target%%i! set /a fitness+=1 exit /b %fitness%
- mutate
set tempcount=0 set returnarray=tempparent set "%returnarray%=" for %%i in (%*) do (
set /a tempcount+=1 set %returnarray%!tempcount!=%%i set %returnarray%=!%returnarray%! %%i
) set /a tomutate=%random% %% 28 + 1 set /a mutateto=%random% %% 27 + 1 set tempcount=0 for %%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 </lang>
- 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
<lang bbcbasic> 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$(C%) ENDIF = text$</lang>
C
<lang 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; }</lang>output<lang>iter 0, score 26: WKVVYFJUHOMQJNZYRTEQAGDVXKYC iter 1, score 25: WKVVTFJUHOMQJN YRTEQAGDVSKXC iter 2, score 25: WKVVTFJUHOMQJN YRTEQAGDVSKXC iter 3, score 24: WKVVTFJUHOMQJN YRTEQAGDVAKFC ... iter 221, score 1: METHINKSHIT IS LIKE A WEASEL iter 222, score 1: METHINKSHIT IS LIKE A WEASEL iter 223, score 0: METHINKS IT IS LIKE A WEASEL</lang>
C++
<lang cpp>#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_chars void 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 probability std::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 // second bool 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";
}</lang> Example output:
BBQYCNLDIHG RWEXN PNGFTCMS: -203 ECPZEOLCHFJBCXTXFYLZQPDDQ KP: -177 HBSBGMKEEIM BUTUGWKWNRCGSZNN: -150 EEUCGNKDCHN RSSITKZPRBESYQK: -134 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#
<lang csharp>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 = (from child in Enumerable.Repeat(parent, C) select Mutate(child, P)) .Concat(Enumerable.Repeat(parent, 1));
// Sort the strings by the fitness function. var sorted = from candidate in candidates orderby Fitness(target, candidate) descending select candidate;
// New parent is the most fit candidate. parent = sorted.First();
++i; Console.WriteLine(" #{0,6} {1,20} fitness: {2}", i, parent, Fitness(target, parent)); }
Console.WriteLine("END: #{0,6} {1,20}", i, parent); }
}</lang>
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
<lang 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!");
}</lang>
Clojure
Define the evolution parameters (values here per Wikipedia article), with a couple of problem constants. <lang clojure>(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")</lang> Now the major functions. fitness simply counts the number of characters matching the target. <lang clojure>(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))</lang> Finally evolve. At each generation, print the generation number, the parent, and the parent's fitness. <lang clojure>(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))))</lang>
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. <lang cobol>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.</lang>
- 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
<lang cfm> <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#
</Cfoutput><cfflush> </Cfif>
</Cfloop> </lang>
#1 7% fit: VOPJOBSYPTTUNYYSAFHTPJUIAIL #2 18% fit: FQUFHEKPLXTQISYZZRIEVQWBHRC #3 21% fit: MGTUKIRICATKDDMSIUNDERUAASKT #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
<lang 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))))</lang>
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): <lang lisp>(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")</lang>outupt<lang> 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)</lang>
D
<lang 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); }
}</lang>
- 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
<lang e>pragma.syntax("0.9") pragma.enable("accumulator")
def target := "METHINKS IT IS LIKE A WEASEL" def alphabet := "ABCDEFGHIJKLMNOPQRSTUVWXYZ " def C := 100 def 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()</lang>
EchoLisp
<lang scheme> (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)) )) </lang>
- 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 3.2 : <lang elena>import system'routines. import extensions.
const Target = "METHINKS IT IS LIKE A WEASEL". const AllowedCharacters = " ABCDEFGHIJKLMNOPQRSTUVWXYZ".
const C = 100. const P = 0.05r.
rnd = randomGenerator.
randomChar
= AllowedCharacters[rnd nextInt(AllowedCharacters length)].
extension evoHelper {
randomString = 0 till:self repeat(:x)( randomChar ); summarize:(String new); literal. fitnessOf:s = self zip:s by(:a:b)( (a == b)iif(1,0) ); summarize(Integer new); int. mutate : p = self selectBy(:ch)( (rnd nextReal <= p) iif(randomChar,ch) ); summarize(String new); literal.
}
class EvoAlgorithm :: Enumerator {
object theTarget. object theCurrent. object theVariantCount.
constructor new : s of:count [ theTarget := s. theVariantCount := count int. ]
get = theCurrent.
next [ if ($nil == theCurrent) [ theCurrent := theTarget length; randomString. ^ true ]. if (theTarget == theCurrent) [ ^ false ]. var variants := Array new:theVariantCount; populate(:x)( theCurrent mutate:P ). theCurrent := variants array; sort(:a:b)( a fitnessOf:Target > b fitnessOf:Target ); getAt:0. ^ true. ]
}
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.
].</lang>
- 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
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.
<lang Elixir>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" end
end
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")</lang>
- 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
<lang 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]).
</lang>
Euphoria
<lang 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 s
end 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 s
end 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 = 15 sequence parent, specimen integer iter, best atom fit, best_fit parent = random_generation(length(target)) iter = 0 while 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 += 1
end while printf(1,"Finally, \"%s\"\n",{parent})</lang>
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#
<lang fsharp>let target = "METHINKS IT IS LIKE A WEASEL" let charset = "ABCDEFGHIJKLMNOPQRSTUVWXYZ "
let rand = System.Random()
let fitness (trial: string) =
Seq.zip target trial |> Seq.fold (fun d (c1, c2) -> if c1=c2 then d+1 else d) 0
let mutate parent rate _ =
String.map (fun c -> if rand.NextDouble() < rate then c else charset.[rand.Next charset.Length]) parent
do
let mutable parent = String.init target.Length (fun _ -> charset.[rand.Next charset.Length] |> string) let mutable i = 0 while parent <> target do let pfit = fitness parent let best, f = Seq.init 200 (mutate parent (float pfit / float target.Length)) |> Seq.map (fun s -> (s, fitness s)) |> Seq.append [parent, pfit] |> Seq.maxBy (fun (_, f) -> f) if i % 100 = 0 then printf "%5d - '%s' (fitness:%2d)\n" i parent f parent <- best i <- i + 1 printf "%5d - '%s'\n" i parent</lang>
Output is:
0 - 'CEUMIDXSIXOOTSEHHXVMD IHTFWP' (fitness: 6) 100 - 'PEPHIZLB NGSIO LCWE AQEKCSZQ' (fitness:11) 200 - 'MESHIZHB IQ IO LTWGGAQWMKSRX' (fitness:13) 300 - 'MESHIZHB IQ IO LTWGGAQWMKSRX' (fitness:13) 400 - 'METHIVKS ITLIN LYKJPABWDASEU' (fitness:19) 500 - 'METHINKS IT IB LIKEFA WDASEL' (fitness:25) 518 - 'METHINKS IT IS LIKE A WEASEL' Press any key to continue . . .
Fantom
<lang 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) }
} </lang>
Forth
<lang forth>include lib/choose.4th
\ target string
s" METHINKS IT IS LIKE A WEASEL" sconstant target
27 constant /charset \ size of characterset 29 constant /target \ size of target string 32 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 + c@ ;
\ 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) r@ ( a1 a2 n) while ( a1 a2) over c@ over c@ = ( 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 ( --) r@ ( n) while ( --) r@ 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) r@ ( a1 a2 a3 n) while ( a1 a2 a3) over c@ over c@ = ( a1 a2 a3 f) swap >r >r over r> ( a1 a2 a1 f) if over c@ 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</lang> Output:
habe@linux-471m:~> 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
<lang fortran>
!*************************************************************************************************** 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 !***************************************************************************************************
</lang>
The output is:
<lang>
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
</lang>
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. <lang go>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 r func 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)) } } }
}</lang> 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
Haskell
<lang Haskell>import System.Random import Control.Monad import Data.List import Data.Ord import Data.Array
showNum :: (Num a, Show a) => Int -> a -> String showNum 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 target mutateRate = 20 popsize = 100 charSet = 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</lang>
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.
<lang Haskell>import System.Random import Data.List import Data.Ord import Data.Array import Control.Monad import Control.Arrow
target = "METHINKS IT IS LIKE A WEASEL" mutateRate = 0.1 popSize = 100 printEvery = 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</lang> 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
<lang icon>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 fit end
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 s end
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_fitness end
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 </lang>
J
Note that this is a limited implementation of evolution. We only see changes from mutation - we make no attempt to combine attributes from other survivors. And, in fact, in these implementations we only allow one survivor from each generation.
Solution:
Using sum of differences from the target for fitness, i.e. 0
is optimal fitness.
<lang j>CHARSET=: 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '
NPROG=: 100 NB. number of progeny (C)
MRATE=: 0.05 NB. mutation rate
create =: (?@$&$ { ])&CHARSET NB. creates random list from charset of same shape as y fitness =: +/@:~:"1 copy =: # ,: mutate =: &(>: $ ?@$ 0:)(`(,: create))} NB. adverb select =: ] {~ (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</lang>
Example usage:
Returns list of best solutions at each generation until converged.
<lang j> filter=: {: ,~ ({~ i.@>.&.(%&20)@#) NB. take every 20th and last item
filter evolve 'METHINKS IT IS LIKE A WEASEL'
XXURVQXKQXDLCGFVICCUA NUQPND MEFHINVQQXT IW LIKEUA WEAPEL METHINVS IT IW LIKEUA WEAPEL METHINKS IT IS LIKE A WEASEL</lang>
Alternative solution:
Using explicit versions of mutate
and evolve
above.
<lang j>CHARSET=: 'ABCDEFGHIJKLMNOPQRSTUVWXYZ '
NPROG=: 100 NB. "C" from specification
fitness=: +/@:~:"1 select=: ] {~ (i. <./)@:fitness NB. select fittest member of population populate=: (?@$&# { ])&CHARSET NB. get random list from charset of same length as y log=: [: smoutput [: ;:inv (('#';'fitness: ';'; ') ,&.> ":&.>)
mutate=: dyad define
idxmut=. I. x >: (*/$y) ?@$ 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
)</lang>
Example Usage: <lang j> 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 WEASEL
METHINKS IT IS LIKE A WEASEL</lang>
Java
(Close)
<lang java5> 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); }
}</lang> 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
<lang javascript>// ------------------------------------- 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 gene fitness = {
// 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 + "
");
} result = evolver.run(showProgress);</lang> 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
<lang Julia>function evolve(parent,target,mutation_rate,num_children) println("Initial parent is $parent, its fitness is $(fitness(parent,target))") gens=0 while parent!=target children=[mutate(parent,mutation_rate) for i=1:num_children] bestfit,best=findmax(map(child->fitness(child,target),children)) 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
fitness(s1,s2)=count(x->x,convert(Array{Char,1},s1).==convert(Array{Char,1},s2))
function mutate(s,rate) new_s="" for c in s new_s*=string(rand()<rate? " ABCDEFGHIJKLMNOPQRSTUVWXYZ"[rand(1:27)]:c) end return new_s end
evolve("IU RFSGJABGOLYWF XSMFXNIABKT","METHINKS IT IS LIKE A WEASEL",0.08998,100)</lang> One possible output: <lang Julia>Initial parent is IU RFSGJABGOLYWF XSMFXNIABKT, its fitness is 1 After 10 generations, the new parent is M TBINGJ IGOYSYV KIAM WIAXEL and its fitness is 13 After 20 generations, the new parent is MRTBINKJ IT SYO KT Z WEAIEL and its fitness is 18 After 30 generations, the new parent is MGTHINKJ IT ISYLMKJ A WEASEL and its fitness is 23 After 40 generations, the new parent is MBTHINKS IT ISYLIKE A WEASEL and its fitness is 26 After 49 generations, the parent evolved into the target METHINKS IT IS LIKE A WEASEL</lang>
Kotlin
<lang scala>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 $i generations")
}</lang>
Liberty BASIC
<lang lb>C = 10 'mutaterate has to be greater than 1 or it will not mutate mutaterate = 2 mutationstaken = 0 generations = 0 Dim parentcopies$((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$ Scan
Wend
Print Print "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$, (mutatingcharacter + 1)) End If
End Function
Function Fitness(parent$, offspring$)
For i = 1 To Len(targetString$) If Mid$(parent$, i, 1) <> Mid$(offspring$, i, 1) Then Fitness = (Fitness + 1) End If Next i
End Function</lang>
Logo
<lang 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] [?]] :parent
end
make "C 100 make "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.child
end
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</lang>
Lua
<lang lua>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 score end
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 result end
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 = 0 while 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</lang>
Mathematica / Wolfram Language
<lang Mathematica>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, offspringj, k = RandomChoice[alphabet]], {j, fertility}, {k, StringLength@parent} ]; offspring = StringJoin[#] & /@ offspring; kidfits = fitness[#] & /@ Flatten[{offspring, parent}]; Return[offspring[[First@Ordering[kidfits]]]]; ];
mutationRate = 0.02; parent = StringJoin[ alphabet[[RandomInteger[{1, Length@alphabet}, StringLength@target]]] ]; results = NestWhileList[Mutate[#, mutationRate, 100] &, parent, fitness[#] > 0 &]; fits = fitness[#] & /@ results; results = Transpose[{results, fits}]; TableForm[results;; ;; 2, TableHeadings->{Range[1, Length@results, 2],{"String","Fitness"}}, TableSpacing -> {1, 2}] </lang>
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".
<lang MATLAB>%This class impliments a string that mutates to a target classdef 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 %methods
end %classdef</lang> Sample Output: (Some evolutionary cycles omitted for brevity) <lang MATLAB>>> instance = EvolutionaryAlgorithm('METHINKS IT IS LIKE A WEASEL',.08,50,1000) Target: METHINKS IT IS LIKE A WEASEL Parent: UVEOCXXFBGDCSFNMJQNWTPJ PCVA Valid Alphabet: ABCDEFGHIJKLMNOPQRSTUVWXYZ Number of Children: 50 Mutation Rate [0,1]: 8.000000e-002 Maximum Iterations: 1000
>> evolve(instance); Target fitness is 28 1: MVEOCXXFBYD SFCMJQNWTPM PCVA - Fitness: 2 2: MEEOCXXFBYD SFCMJQNWTPM PCVA - Fitness: 3 3: MEEHCXXFBYD SFCMJXNWTPM ECVA - Fitness: 4 4: MEEHCXXFBYD SFCMJXNWTPM ECVA - Fitness: 4 5: METHCXAFBYD SFCMJXNWXPMARPVA - Fitness: 5 6: METHCXAFBYDFSFCMJXNWX MARSVA - Fitness: 6 7: METHCXKFBYDFBFCQJXNWX MATSVA - Fitness: 7 8: METHCXKFBYDFBF QJXNWX MATSVA - Fitness: 8 9: METHCXKFBYDFBF QJXNWX MATSVA - Fitness: 8 10: METHCXKFUYDFBF QJXNWX MITSEA - Fitness: 9 20: METHIXKF YTBOF LIKN G MIOSEI - Fitness: 16 30: METHIXKS YTCOF LIKN A MIOSEL - Fitness: 19 40: METHIXKS YTCIF LIKN A MEUSEL - Fitness: 21 50: METHIXKS YT IS LIKE A PEUSEL - Fitness: 24 100: METHIXKS YT IS LIKE A WEASEL - Fitness: 26 150: METHINKS YT IS LIKE A WEASEL - Fitness: 27 195: METHINKS IT IS LIKE A WEASEL - Fitness: 28</lang>
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. <lang MATLAB> %% 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 window tic % Begins the timer
%% Select Target String target = '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 Size mutRate = .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 Needed ideal = 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 Population Pop = 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 </lang>
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%). <lang MATLAB> Gen: 1 | Fitness: 465 | C�I1%G+<%?R�8>9�JU#(E�UO�PHI Gen: 2 | Fitness: 429 | W=P6>D�I)VU6$T 99,� B�BMP0JH Gen: 3 | Fitness: 366 | P�;R08AS�GJ�IS&T38IE�)SJERLJ Gen: 4 | Fitness: 322 | KI8M5LAS�GJ�IS�SP�@)D�V@ JCP Gen: 5 | Fitness: 295 | UAUR08AS�GJ�IS�8HG*�+�=C?UB( Gen: 6 | Fitness: 259 | VCUQH35S�HR4.L�ISJQ%J�OC*T=E Gen: 7 | Fitness: 226 | LFB8GPET(LODKQ�KQ<K E*PEMA6I Gen: 8 | Fitness: 192 | EPKOLCIR�QQ�NF�QG:B(D/U>BQGF Gen: 9 | Fitness: 159 | N8R7?SOU�NO$OK O?K?!;�MB?QHG Gen: 10 | Fitness: 146 | TGN@EQR4)PS%IS#TFJQ%A!U>BVLI Gen: 11 | Fitness: 120 | L?VMALJS%?R EK IILE�6'RRERLJ Gen: 12 | Fitness: 102 | R@T9COMR�NU CS*R?K?!; VD>LCL Gen: 13 | Fitness: 96 | NENMVOMR�NU CS*R?K?!; VD>LCL Gen: 14 | Fitness: 82 | REJGNPMU�KR CS JKI@+D�UD?QHG Gen: 15 | Fitness: 75 | NETI=HPQ�FT ID EFKE D"WD>QDQ Gen: 16 | Fitness: 70 | R@TKCOOT)@R$IS KKLE�D"WC?UBJ Gen: 17 | Fitness: 61 | NESIKQRP�NU CS�MFKE ; SEETCP Gen: 18 | Fitness: 57 | LFSGLPTN�NU GQ IIKE D"VD>LCL Gen: 19 | Fitness: 40 | NENKJLMS�GS%IS#MFKE B UFATCL Gen: 21 | Fitness: 39 | NETIGPEU�KR IS IIKD"? UFDQEK Gen: 22 | Fitness: 33 | NETGCOMT�LU IS#MFKE B UFATCL Gen: 23 | Fitness: 32 | NETIKNPQ�NU IS#IIKE B UFATCL Gen: 24 | Fitness: 27 | NETKJLMS�LU IS MFKE B UFATCL Gen: 25 | Fitness: 23 | LETIKOMS LU IS IIKE D WEDQEK Gen: 26 | Fitness: 22 | NETIKMJS LU IS IIKE D WEDQEK Gen: 27 | Fitness: 20 | LETIKOMS LU IS KILE B"WFATCL Gen: 28 | Fitness: 19 | NESGJQJS�GU IS KIKE B WFATEK Gen: 29 | Fitness: 16 | NETIHPMS KR IS KIKE B WFATEK Gen: 30 | Fitness: 15 | NESHLPKS KU IS KIKE B WFATEK Gen: 31 | Fitness: 13 | NETGGNKS KU IS KIKE C WFATEK Gen: 32 | Fitness: 12 | NETHGNJS IU IS JIKE B WFATCL Gen: 33 | Fitness: 11 | NETIJPKS IU IS KIKE B WFATEK Gen: 35 | Fitness: 8 | LEUIHNJS IT IS JIKE A WEATEL Gen: 37 | Fitness: 7 | NETIHNJS IS IS LIKE B WFASEL Gen: 38 | Fitness: 6 | NETHGNJS IT IS LIKE A WFASEK Gen: 39 | Fitness: 4 | METGHNKS IT IS LIKE B WEATEL Gen: 42 | Fitness: 3 | NETHINKS IT IS KIKE B WEASEL Gen: 43 | Fitness: 2 | NETHINKS IT IS LIKE A WFASEL Gen: 44 | Fitness: 1 | METHHNKS IT IS LIKE A WEASEL Gen: 46 | Fitness: 0 | METHINKS IT IS LIKE A WEASEL Elapsed time is 0.099618 seconds. </lang>
Nim
<lang nim>import math, os randomize()
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 = 0 while 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</lang>
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
<lang objeck>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(); } } }
}</lang>
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
<lang ocaml>let target = "METHINKS IT IS LIKE A WEASEL" let charset = "ABCDEFGHIJKLMNOPQRSTUVWXYZ " let tlen = String.length target let clen = String.length charset let () = 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</lang>
Octave
<lang octave>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; endif
endfunction
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); endif
endwhile disp(parent'); </lang>
Oforth
<lang 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 ;</lang>
- 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. <lang oorexx> /* 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 initialize generation = 0 do 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 + 1
end exit
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) end
return
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 end
return fitness
mutate:procedure expose alphabet arg 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) end
return result </lang> 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. <lang oxygenbasic>
'EVOLUTION
target="METHINKS IT IS LIKE A WEASEL" le=len target progeny=string le,"X"
quad seed declare 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 edx
End 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 target
end do print progeny " " gens 'RESULT (range 1200-6000 generations) </lang>
Oz
<lang 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}} end
in
{Main}</lang>
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. <lang parigp>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)</lang>
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.
<lang pascal>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.</lang>
Perl
This implementation usually converges in less than 70 iterations.
<lang perl>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";}</lang>
Perl 6
<lang perl6>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 }</lang>
Phix
<lang 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 s
end 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 += 1
end while printf(1,"Finally, \"%s\"\n",{parent})</lang>
- 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" <lang PicoLisp>(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</lang>
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
<lang 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);
</lang> 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.)
<lang Pike>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; } }
}</lang>
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
<lang Pony>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</lang>
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.
<lang Pony>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())</lang>
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
<lang Prolog>target("METHINKS IT IS LIKE A WEASEL").
rndAlpha(64, 32). % Generate a single random character rndAlpha(P, P). % 32 is a space, and 65->90 are upper case rndAlpha(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).</lang> 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
<lang PureBasic>Define population = 100, mutationRate = 6 Define.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 fit
EndProcedure
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 Next
EndProcedure
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 S
EndProcedure
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$) - 1 maxfit = Len(target$) target_len = Len(target$) - 1 Dim 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() <> ""</lang>
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. <lang python>from string import letters from random import choice, random
target = list("METHINKS IT IS LIKE A WEASEL") charset = letters + ' ' parent = [choice(charset) for _ in range(len(target))] minmutaterate = .09 C = 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 = 0 center = len(C)/2 while 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()</lang>
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: <lang python>from random import choice, random
target = list("METHINKS IT IS LIKE A WEASEL") alphabet = " ABCDEFGHIJLKLMNOPQRSTUVWXYZ" p = 0.05 # mutation probability c = 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 = 0 print "%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</lang>
R
<lang R>set.seed(1234, kind="Mersenne-Twister")
- Easier if the string is a character vector
target <- 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 generation
C <- 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) childrenwhich.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)</lang>
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
Racket
<lang 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)) </lang>
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.
<lang rexx>/*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); say say 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 $</lang>
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.
<lang rexx>/*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); say say 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 $</lang>
output is the same as the previous version.
Ruby
for the sample
method.
<lang ruby>@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}.join
end
def log(iteration, rate, parent)
puts "%4d %.2f %5.1f %s" % [iteration, rate, fitness(parent), parent]
end
iteration = 0 parent = Array.new(@target.length) {random_char}.join prev = ""
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)}
end log(iteration, rate, parent)</lang>
- 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
Scala
<lang scala>import scala.annotation.tailrec
case class LearnerParams(target:String,rate:Double,C:Int)
val chars = ('A' to 'Z') ++ List(' ') val randgen = new scala.util.Random def 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 }
@tailrec def 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) mkString evolve(0,initial)</lang>
Scheme
<lang 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))
</lang>
Seed7
<lang 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;</lang>
SequenceL
SequenceL Code:
<lang sequencel>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))];</lang>
C++ Driver Code:
<lang cpp>#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; }</lang>
- 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" #17: "MEVSWRHSXIK IA LVAESACWOADEL" #18: "MEVIWRHSXIK IA LVAESACWOADEL" #19: "MENIWRHSXIK IA LVAE ACWOADEL" #20: "MENIWRHSXIK IA LVAE ACWOADEL" #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
<lang ruby>define target = "METHINKS IT IS LIKE A WEASEL" define mutate_chance = 0.08 define 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) }</lang>
Smalltalk
<lang smalltalk>Object subclass: Evolution [
|target parent mutateRate c alphabet fitness|
Evolution class >> newWithRate: rate andTarget: aTarget [ |r| r := super new. ^r initWithRate: rate andTarget: aTarget. ]
initWithRate: rate andTarget: aTarget [ target := aTarget. self mutationRate: rate. self maxCount: 100. self defaultAlphabet. self changeParent. self fitness: (self defaultFitness). ^self ]
defaultFitness [ ^ [:p :t | |t1 t2 s| t1 := p asOrderedCollection. t2 := t asOrderedCollection. s := 0. t2 do: [:e| (e == (t1 removeFirst)) ifTrue: [ s:=s+1 ] ]. s / (target size) ] ]
defaultAlphabet [ alphabet := 'ABCDEFGHIJKLMNOPQRSTUVWXYZ ' asOrderedCollection. ]
maxCount: anInteger [ c := anInteger ]
mutationRate: aFloat [ mutateRate := aFloat ]
changeParent [ parent := self generateStringOfLength: (target size) withAlphabet: alphabet. ^ parent. ]
generateStringOfLength: len withAlphabet: ab [ |r| r := String new. 1 to: len do: [ :i | r := r , ((ab at: (Random between: 1 and: (ab size))) asString) ]. ^r ]
fitness: aBlock [ fitness := aBlock ]
randomCollection: d [ |r| r := OrderedCollection new. 1 to: d do: [:i| r add: (Random next) ]. ^r ]
mutate [ |r p nmutants s| r := parent copy. p := self randomCollection: (r size). nmutants := (p select: [ :e | (e < mutateRate)]) size. (nmutants > 0) ifTrue: [ |t| s := (self generateStringOfLength: nmutants withAlphabet: alphabet) asOrderedCollection. t := 1. (p collect: [ :e | e < mutateRate ]) do: [ :v | v ifTrue: [ r at: t put: (s removeFirst) ]. t := t + 1. ] ]. ^r ]
evolve [ |children es mi mv| es := self getEvolutionStatus. children := OrderedCollection new. 1 to: c do: [ :i | children add: (self mutate) ]. children add: es. mi := children size. mv := fitness value: es value: target. children doWithIndex: [:e :i| (fitness value: e value: target) > mv ifTrue: [ mi := i. mv := fitness value: e value: target ] ]. parent := children at: mi. ^es "returns the parent, not the evolution" ]
printgen: i [ ('%1 %2 "%3"' % {i . (fitness value: parent value: target) . parent }) displayNl ]
evoluted [ ^ target = parent ] getEvolutionStatus [ ^ parent ]
].
|organism j|
organism := Evolution newWithRate: 0.01 andTarget: 'METHINKS IT IS LIKE A WEASEL'.
j := 0. [ organism evoluted ]
whileFalse: [ j := j + 1. organism evolve. ((j rem: 20) = 0) ifTrue: [ organism printgen: j ] ].
organism getEvolutionStatus displayNl.</lang>
Tcl
<lang tcl>package require Tcl 8.5
- A function to select a random character from an argument string
proc tcl::mathfunc::randchar s {
string index $s [expr {int([string length $s]*rand())}]
}
- Set up the initial variables
set target "METHINKS IT IS LIKE A WEASEL" set charset "ABCDEFGHIJKLMNOPQRSTUVWXYZ " set parent [subst [regsub -all . $target {[expr {randchar($charset)}]}]] set MaxMutateRate 0.91 set C 100
- Work with parent and target as lists of characters so iteration is more efficient
set 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 naming
proc 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</lang> 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).
<lang tcl>package require Tcl 8.5
proc 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 0.91 set 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 string
interp alias {} fitness {} fitnessByEquality [split $target {}]
- This generates the converse of the Python version; logically saner naming
proc 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 printer
proc prettyPrint {iterations parent} {
puts [format "#%-4i, fitness %5.1f%%, '%s'" $iterations \
[expr {[fitness $parent]*100}] [join $parent {}]] }
- The evolutionary algorithm itself
proc 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</lang>
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. <lang>T = 0 ' Address of target L = 28 ' Length of string P = T + L ' Address of parent R = 6 ' Mutation rate in percent C = 7 ' Number of children B = 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 length
Loop
End
_MutateDNA Param(3) ' Mutate an entire DNA
Local(1)
For d@ = 0 to L-1 ' For the entire string If c@ > Rnd(100) Then ' If mutation rate is met @(a@*L+d@) = Ord("A") + Rnd(27) ' Mutate the gene Else @(a@*L+d@) = @(b@+d@) ' Otherwise copy it from the parent EndIf Next
Return
_Fitness Param(2) ' Check for fitness
Local(2)
c@ = 0 ' Fitness is zero For d@ = 0 to L-1 ' For the entire string If @(a@*L+d@) = @(b@+d@) Then c@ = c@ + 1 Next ' If string matches, increment score
Return (c@) ' Return the fitness
_MakeParent Param(2) ' Make a child into a parent
Local(1)
For c@ = 0 to L-1 ' For the entire string @(a@+c@) = @(b@*L+c@) ' Copy the DNA gene by gene Next
Return
_PrintParent Param(1) ' Print the parent
Local(1)
For b@ = 0 to L-1 ' For the entire string If (@(a@+b@)) > Ord ("Z") Then Print " "; ' Cater for the space Else Print CHR(@(a@+b@)); ' Print a gene EndIf Next
Print ' Issue a linefeed
Return
_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 DNA
Return</lang>
- 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.
<lang Ursala>#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</lang> output:
'METHINKS IT IS LIKE A WEASEL'
vbscript
<lang 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
Randomize Parent = ""
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</lang>
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 <lang Visual Basic>
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 = Text
End Function Function 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 </lang>
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
<lang XPL0>include c:\cxpl\codes; \intrinsic code declarations string 0; \use zero-terminated convention (instead of MSb)
def MutateRate = 15, \1 chance in 15 of a mutation
Copies = 30; \number of mutated copies
char Target, AlphaTbl; int SizeOfAlpha;
func StrLen(Str); \Return the number of characters in a string
char 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 B
char 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 having
char A, B; \ a 1 in MutateRate chance of differing from A
int 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 string for 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; ]</lang>
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
<lang zkl>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 worser fcn 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);</lang>
- 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
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