use AI::Genetic::Pro;

my @costs = map [split], <DATA>;
my $NUM_OF_NODES = @costs;
my $GROUP_SIZE = $NUM_OF_NODES/2;

my @connections = sort {$a <=> $b }
	map
		{ my $r=$_; map { $costs[$r][$_] } $_+1..$NUM_OF_NODES-1 }
	0..$NUM_OF_NODES-1;

my $numbetweengroups = $GROUP_SIZE * $GROUP_SIZE;
my $numinsidegroups = @connections - $numbetweengroups;

my $ideal_score = 0;
my $all_conn_sum = 0;

for(0..$#connections)
{
	if($_<$numbetweengroups)
		{ $ideal_score += $connections[$_] }
	$all_conn_sum += $connections[$_];
}

##</code><code>##

sub fitness
{
	my ($ga, $chromosome) = @_;
	my @queue = $ga->as_array($chromosome);
	my @group1ids = @queue[0..$GROUP_SIZE-1];
	my @group2ids = @queue[$GROUP_SIZE..$NUM_OF_NODES-1];
	my $current_score = 0;
	for my $src(@nodes[@group1ids])
	{
		for my $dst(@nodes[@group2ids])
		{
			$current_score += $costs[$src][$dst]
		}
	}
	return $all_conn_sum - $current_score;
}

##</code><code>##

sub terminate
{
	my ($ga) = @_;
	my $result = $all_conn_sum - fitness($ga, $ga->getFittest);
	return $result == $ideal_score ? 1 : 0;
}

##</code><code>##

my $ga = AI::Genetic::Pro->new(
	-fitness         => \&fitness,        # fitness function
	-terminate       => \&terminate,      # terminate function
	-type            => 'combination',    # type of chromosomes
	-population      => 1000,             # population
	-crossover       => 0.9,              # probab. of crossover
	-mutation        => 0.01,             # probab. of mutation
	-parents         => 2,                # number  of parents
	-selection       => [ 'Roulette' ],   # selection strategy
	-strategy        => [ 'OX' ],         # crossover strategy
	-cache           => 0,                # cache results
	-history         => 1,                # remember best results
	-preserve        => 3,                # remember the bests
);

##</code><code>##

$ga->init([0..$#costs]);

##</code><code>##

$ga->evolve(10);

##</code><code>##

print "SCORE: ", $ga->as_value($ga->getFittest), " for set: ", "[".join(", ", $ga->as_array($ga->getFittest))."]", ".\n";

<p>And if someone want's to see history of evolution:</p>
# $ga->chart(-filename => 'evolution.png');

##</code><code>##

use AI::Genetic::Pro;

my @costs = map [split], <DATA>;
my $NUM_OF_NODES = @costs;
my $GROUP_SIZE = $NUM_OF_NODES/2;

my @connections = sort {$a <=> $b }
	map
		{ my $r=$_; map { $costs[$r][$_] } $_+1..$NUM_OF_NODES-1 }
	0..$NUM_OF_NODES-1;

my $numbetweengroups = $GROUP_SIZE * $GROUP_SIZE;
my $numinsidegroups = @connections - $numbetweengroups;

my $ideal_score = 0;
my $all_conn_sum = 0;

for(0..$#connections)
{
	if($_<$numbetweengroups)
		{ $ideal_score += $connections[$_] }
	$all_conn_sum += $connections[$_];
}

sub fitness
{
	my ($ga, $chromosome) = @_;
	my @queue = $ga->as_array($chromosome);
	my @group1ids = @queue[0..$GROUP_SIZE-1];
	my @group2ids = @queue[$GROUP_SIZE..$NUM_OF_NODES-1];
	my $current_score = 0;
	for my $src(@nodes[@group1ids])
	{
		for my $dst(@nodes[@group2ids])
		{
			$current_score += $costs[$src][$dst]
		}
	}
	return $all_conn_sum - $current_score;
}

sub terminate
{
	my ($ga) = @_;
	my $result = $all_conn_sum - fitness($ga, $ga->getFittest);
	return $result == $ideal_score ? 1 : 0;
}

my $ga = AI::Genetic::Pro->new(
	-fitness         => \&fitness,        # fitness function
	-terminate       => \&terminate,      # terminate function
	-type            => 'combination',    # type of chromosomes
	-population      => 1000,             # population
	-crossover       => 0.9,              # probab. of crossover
	-mutation        => 0.01,             # probab. of mutation
	-parents         => 2,                # number  of parents
	-selection       => [ 'Roulette' ],   # selection strategy
	-strategy        => [ 'OX' ],         # crossover strategy
	-cache           => 0,                # cache results
	-history         => 1,                # remember best results
	-preserve        => 3,                # remember the bests
);

# init population of 32-bit vectors
$ga->init([0..$#costs]);

# evolve 10 generations
$ga->evolve(10);

# best score
print "SCORE: ", $ga->as_value($ga->getFittest), " for set: ", "[".join(", ", $ga->as_array($ga->getFittest))."]", ".\n";

    
# save evolution path as a chart
# $ga->chart(-filename => 'evolution.png');

__DATA__
- 159 38 172 76 143 155 78 282 58
159 - 7 264 128 105 42 169 124 153
38 7 - 226 142 85 163 120 74 285
172 264 226 - 48 271 15 151 255 116
76 128 142 48 - 189 152 237 183 10
143 105 85 271 189 - 167 193 18 127
155 42 163 15 152 167 - 99 187 59
78 169 120 151 237 193 99 - 51 12
282 124 74 255 183 18 187 51 - 281
58 153 285 116 10 127 59 12 281 -
##</code><code>##

#!perl -l

use AI::Genetic;
my $ga = new AI::Genetic
	(
		-fitness    => \&fitnessFunc,
		-type       => 'rangevector',
		-population => 50,
		-crossover  => 0.9,
		-mutation   => 0.01,
		-terminate  => \&terminateFunc,
	);

	# 3x^2+2x-5
@aPoints = ([-2,3], [-1,-4], [0,-5], [1,0], [2,11], [3,28]);
$ga->init([[-10, 31], [1, 3], [-10, 31], [1, 3], [-10, 31], [1, 3], [-10, 31], [1, 3], [-10, 31]]);
sub translate
{
	($p, @aa) = @_;
	
	%operators = (1=>'+',2=>'-',3=>'*');
	
	while(@aa)
	{
		$operator = shift @aa;
		$value = shift @aa;
		$value = '$x' if $value > 10;
		$p .= $operators{$operator}.'('.$value.')';
	}
	# substitute (12) -> 12
	$p=~s/\((\d+|\$x)\)/$1/g;
	# substitute -(-12) -> +12
	$p=~s/-\(-(\d+|\$x)\)/+$1/g;
	# substitute +(-12) -> -12
	$p=~s/\+\((-\d+|-\$x)\)/$1/g;
	# remove +0 lub -0
	$p=~s/(\+|-)0(\+|-)/$2/g;
	# print $p;
	$p
}
$ga->evolve('rouletteTwoPoint', 300);
print "Best score = ", $ga->getFittest->score, ", for genes: ", translate(@{$ga->getFittest->genes}), ".\n";

sub fitnessFunc
{
	my $genes = shift;

	my $equation = translate(@$genes);
	
	my $fitness = 0;
	for(@aPoints)
	{
		($x, $y) = @$_;
		$fitness -= abs($y - eval $equation);
	}

	return $fitness;
}

sub terminateFunc
{
	my $ga = shift;

	# terminate if reached result.
	return 1 if $ga->getFittest->score == @aPoints;
	return 0;
}