AI-Genetic-Pro
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t/13_preserve.t view on Meta::CPAN
use strict;
use warnings;
use FindBin qw($Bin);
use lib $Bin, $Bin.'../lib';
use Test::More qw(no_plan);
use AI::Genetic::Pro;
use constant BITS => 32;
my @Win;
push @Win, 1 for 1..BITS;
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 => 'bitvector', # 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 => 4, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
my @Win0 = @Win; $Win0[-1] = 0;
my @Win1 = @Win; $Win1[-2] = 0;
my @Win2 = @Win; $Win2[-1] = 0; $Win2[-2] = 0;
$ga->inject( [ \@Win, \@Win0, \@Win1, \@Win2 ] );
$ga->evolve(1);
my $count = 0;
for my $w (\@Win, \@Win0, \@Win1, \@Win2){
my $s = $ga->as_string($w);
foreach my $c (@{$ga->chromosomes}){
if($ga->as_string($c) eq $s){
$count++;
last;
}
}
}
ok($count == 4);
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