Genetic Algorithm Example from Igor Book

#pragma moduleName = FindSecret

//----
//--There are other (better) methods for solving the task of
//--getting a 'secret' word. The genetic approach
//--is, however, very instructive from a
//--programmer's point of view. That is why it is done here.

static function main(generations,[mutationProb,individuals])

    variable generations
    variable mutationProb
    variable individuals
   
    mutationProb = (ParamIsDefault(mutationProb) ? 0.05 : mutationProb)
    individuals = (ParamIsDefault(individuals) ? 1000 : individuals)
     
    string secret = "Santa Claus is coming!"
    variable lengthSecret = strlen(secret)
    variable i,k,j
    variable parentIndex
    string gene =""
    string parentGenes = ""
   
    // make a two-dimensional text-wave to store the "genepool"
    // the first column stores a string with the individual's genes,
    // the second column stores the number of correct characters
    // "Santa Clerx" "8"
    // if the number of correct characters == lengthSecret
    // the solution was found!
       
    print "----------------------------------- Starting search!"
    print " "  
   
    //set up a genepool as a free (transient) 2D-textwave
   
    make /FREE /T /N=(individuals,2) GenePool
    wave /T gP = GenePool
   
    //
    // make the first generation
    //

    for (i=0; i<individuals; i++)
        gP[i][0]=""
        gP[i][1]="0"
        for (k=0; k<lengthSecret; k++)
           
            //get a random number between 30 and 127
            //and interpret it as ASCII code by using num2char
            //(0.5 + enoise(0.5)) generates a random number between 0 and 1
            //use floor for rounding to integer values
   
            gene = num2char(floor(30+97*(0.5+enoise(0.5))))
            gP[i][0] += gene
           
            //test if this newest gene is correct, if yes: increase score by one
            if(char2num(gene) == char2num(secret[k]))
                gP[i][1] = num2str( str2num( gP[i][1] ) + 1 )
            endif  
        endfor
    endfor
    SortColumns /A /R /KNDX=1 sortWaves=gP
   
    //sort by score, i.e., sort by number of correct characters
    //without '/A' 1,13,9 is sorted as 9,13,1
    //(sorting by the first character)
   
    //
    //now do the further generations
    //
   
    for (j=0; j<generations; j++)
   
        //leave the best 10% of the previous generation untouched
        //start the for loop at a higher i
       
        for (i=floor(0.1*individuals); i<individuals; i++)  
            gP[i][0]=""
            gP[i][1]="0"
           
            for (k=0; k<lengthSecret; k++)
               
                //allow a mutation with a certain probability
               
                if ( (0.5+enoise(0.5)) < mutationProb)
                    //draw again a random ASCII character
                    gene = num2char(floor(30+97*(0.5+enoise(0.5))))
                    gP[i][0] += gene
                   
                    if (GrepString(gene,secret[k]))
                        gP[i][1] = num2str( str2num( gP[i][1] ) + 1 )
                    endif  
                   
                else //no mutation
                    //pick a random individual from the best 10%
                    parentIndex = floor(0.1*individuals*(0.5+enoise(0.5)))
                    parentGenes = gP[parentIndex][0]
                    gP[i][0] += parentGenes[k]
                   
                    if (char2num(parentGenes[k])==char2num(secret[k]))
                        gP[i][1] = num2str(str2num(gP[i][1])+1)
                    endif  
                                       
                endif  //end mutation-choice

            endfor  //end loop over individual letters (genes)
        endfor //end loop over individuals (rows in genePool)


        SortColumns /A /R /KNDX=1 sortWaves=gP
       
        print "Generation",j+1,":", gP[0][0]   
       
        if (str2num(gP[0][1])==lengthSecret)
                print " "
                print "Puzzle solved! "
                break
        endif
    endfor  //end loop over further generations

end