package MyCritters;

my $size;
BEGIN { $size = shift || 10; }

## set up a default critter class using an array representation:

use ArrayEvolver alphabet => [0 .. $size-1],
                 mutation_rate => 1/$size,
                 gene_length => $size;
@ISA = ('ArrayEvolver');

## add a new method:

sub as_sentence {
    my $critter = shift;
    sprintf "This sentence contains " .
            join(", " => map qq[%d "$_"s], 0 .. $size-1),
            @{ $critter->gene };
}

## override the default fitness method:

sub fitness {
    my $critter  = shift;
    my $sentence = $critter->as_sentence;
    my $differences;
    for (0 .. $size-1) {
        my $count = () = $sentence =~ /($_)/g;
        $differences += abs( $count - $critter->gene->[$_] );
    }
    return $size*$size - $differences;
}

#############
package main;
use Algorithm::Evolve;

## this gets called after every generation:

sub callback {
    my $p = shift;

    printf "score = %d/%d after %d generations\n",
            $p->best_fit->fitness, $size*$size, $p->generations
        unless $p->generations % 100;

    $p->suspend if $p->generations >= 1000
                or $p->best_fit->fitness == ($size*$size);
}   

## set up a population object:

my $p = Algorithm::Evolve->new(
    critter_class    => 'MyCritters',
    selection        => 'roulette',
    replacement      => 'random',
    parents_per_gen  => 10,
    size             => 100,
    random_seed      => shift,
    callback         => \&callback
);
$p->start;

printf "%s (score = %d/%d)\n",
       $p->best_fit->as_sentence, $p->best_fit->fitness, $size*$size;