use strict;
use warnings;
use FindBin qw($Bin);
use lib $Bin;
use Test::More qw(no_plan);
use AI::Genetic::Pro;
use constant SIZE => 8;
use constant MIN => -4;
use constant MAX => 4;
my @Win;
push @Win, MAX for 1..SIZE;
my $Win = sum( \@Win );
sub sum {
my ($ar) = @_;
my $counter = 0;
for(0..$#$ar){
$counter += $ar->[$_] if $ar->[$_];
}
return $counter;
}
sub fitness {
my ($ga, $chromosome) = @_;
return sum(scalar $ga->as_array($chromosome));
}
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'rangevector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
);
my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
[qw( 4 4 0 0 4 4 0 0 )],
[qw( 4 4 4 4 0 0 0 0 )],
[qw( 0 0 0 0 4 4 4 4 )],
);
push @data, @data for 1..SIZE;
$ga->inject(\@data);
# evolve 1000 generations
$ga->evolve(1000);
ok($Win == $ga->as_value($ga->getFittest));