Guys:

This what I got so far for a assignment. I think I used too much worng referencing, this code functions oddly. The fitness of a gene is a percentage, it should maintain highest gene of percentage, but it doesn't do that. Because the gene is an array of 2 arrays. genotype()subroutine returns gene as a reference \@seq; then it evaluate fitness, mutate and crossover by the reference.

But the best fit gene $population\49\ is always replaced by new mutants. This code is partly based on a post of this forum from another monk, I have to acknowledge that.

blokhead: http://www.perlmonks.org/?node_id=298877

Thank you for all your valuable time to read this code. Thank you guys.

#ea Version3

use strict;
use List::Util qw/shuffle/;

my $ca_length = 8;#size of my genotype, . 
my $pop_size   = 50;
my @data_table; # To store inputted data, 
my @indices = (    76,46,21,15,33,20,69,65,40,11,79,22,
                70,43,32,30,57,19,39,37,62,3,4,44,51,
                58,25,72,64,59,48,80,42,38,61,75,49,1,
                18,71,41,2,67,13,45,60,9,7,28,6,8,54,
                50,73,56,53,77,27,16,29,52,10,24,5,26,
                36,23,34,31,47,14,35,12,55,17,0,68,74,
                66,78,63); #number of the columns.
my $mutation_rate = 0.2;

input();
open(FILEOUT, ">>output.txt"); #numbers table which only has 0 and 1.


my @population = sort { fitness($a) <=> fitness($b) }
                 map  { genotype() } 1 .. $pop_size;

my $generations;
while ( $generations++ < 1000 and fitness($population[49]) < 100 ) {
    my @parents = @population[40 .. 49];
    shuffle @parents;

    my @children;
    push @children, crossover( pop @parents, pop @parents )
        while @parents;

    @population[0 .. 4] = map { mutate($_) } @children;

    @population = sort { fitness($a) <=> fitness($b) } @population;
    for my $i (0..$#population){
        print fitness($population[$i])." : $i\n";
    }
    printf "The best fit after %d generations is: %g\n", $generations,
+ fitness($population[49]);
    print "The least fit : ".fitness($population[0]);
    #printf "Average fitness after %d generations is: %g\n",
        #$generations, (sum map { fitness($_) } @population)/@populati
+on;
}

#foreach my $element (@data_index){ #@population
#    print $element."\n";
#}

print FILEOUT $population[49]."\n";
close FILEOUT;
#####

sub fitness {
    my $ref = shift;


    my @ca_rule = @{${$ref}[1]};
    my @data_indices = @{${$ref}[0]};
    my $wrong_count=0;
    my $right_count=0;
    my $predict='';        

    for my $i ( 1 .. $#data_table ) {
        #$aref = $data_table[$i];
        #print $aref;
        for my $j ( 1 .. $#data_indices-1 ) {#row index
        
            my $predict=$ca_rule[bin2dec($data_table[$i-1][$data_indic
+es[$j-1]].$data_table[$i-1][$data_indices[$j]].$data_table[$i-1][$dat
+a_indices[$j+1]])];

            if ($predict ne $data_table[$i][$data_indices[$j]]){
                $wrong_count++;
            }else{
                $right_count++;
            }        
            #print "elt $i $j is $data_table[$i][$j]\n";
        }
    }
    
    #my $result = ($right_count)/($right_count+$wrong_count -1)*100;
    #print "Right: $right_count \n";
    #print "Wrong: $wrong_count \n";
    #print $result."%\n";
    return ($right_count)/($right_count+$wrong_count -1)*100;
}

sub crossover{
        my $temp = ${$_[0]}[0];
        ${$_[0]}[0] = ${$_[1]}[0];
        ${$_[1]}[0] = $temp;              
        return ($_[0],$_[1]);    
}

sub mutate{
    my @string = @{$_[0]};
    for my $i (0.. $#string){
        my $aref = $string[$i];
        #print "\n";
        if (rand() < $mutation_rate){
            my $position1 = int(rand($#{$aref}));
            my $position2 = int(rand($#{$aref}));
            if ($position1 ne $position2){
                my $temp = $string[$i][$position1];
                $string[$i][$position1] = $string[$i][$position2];
                $string[$i][$position2] = $temp;
            }        
        }
    }
    return \@string;
}    

sub genotype {
    my @seq;
    my @gene;
    my @string;
    push @gene, shuffle @indices ;
    for (1..$ca_length){
        $string[$_] = rand() > 0.5 ? 0 : 1 ;
    }
    push @seq, [@gene];
    push @seq, [@string];
    return \@seq;#return a reference of the array.
}

sub input{
    ###### read data table into arrays##################
    open(FILEIN, "table.txt"); #numbers table which only has 0 and 1.
    while(<FILEIN>){#reads line by line from FILE which is the filehan
+dle for data.txt
        chomp;
           my @data_line = split //, $_;
           push @data_table, [@data_line];
    }
    close FILEIN;
}

sub bin2dec{ return oct"0b$_[0]"}
sub dec2bin {return sprintf "%b",shift }
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