AI-Genetic-Pro
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return $result == 4294967295 ? 1 : 0;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 1000, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.01, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 1, # remember best results
-preserve => 3, # remember the bests
-variable_length => 1, # turn variable length ON
-mce => 1, # optional MCE support
-workers => 3, # number of workers (MCE)
);
lib/AI/Genetic/Pro/Crossover/OX.pm view on Meta::CPAN
#=======================================================================
sub save_fitness {
my ($self, $ga, $idx) = @_;
$ga->_fitness->{$idx} = $ga->fitness->($ga, $ga->chromosomes->[$idx]);
return $ga->chromosomes->[$idx];
}
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($chromosomes, $parents, $crossover) = ($ga->chromosomes, $ga->_parents, $ga->crossover);
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
my @children;
#-------------------------------------------------------------------
while(my $elders = shift @$parents){
my @elders = unpack 'I*', $elders;
unless(scalar @elders){
push @children, $chromosomes->[$elders[0]];
next;
}
my @points = sort { $a <=> $b } map { 1 + int(rand $#{$chromosomes->[0]}) } 0..1;
@elders = sort {
lib/AI/Genetic/Pro/Crossover/PMX.pm view on Meta::CPAN
my %seen;
my @dup = grep { if($seen{$_}){ 1 }else{ $seen{$_} = 1; 0} } @$ar;
return \@dup if @dup;
return;
}
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($chromosomes, $parents, $crossover) = ($ga->chromosomes, $ga->_parents, $ga->crossover);
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
my @children;
#-------------------------------------------------------------------
while(my $elders = shift @$parents){
my @elders = unpack 'I*', $elders;
unless(scalar @elders){
push @children, $chromosomes->[$elders[0]];
next;
}
my @points = sort { $a <=> $b } map { 1 + int(rand $#{$chromosomes->[0]}) } 0..1;
@elders = sort {
lib/AI/Genetic/Pro/Crossover/Points.pm view on Meta::CPAN
use warnings;
use strict;
use List::MoreUtils qw(first_index);
#use Data::Dumper; $Data::Dumper::Sortkeys = 1;
#=======================================================================
sub new { bless { points => $_[1] ? $_[1] : 1 }, $_[0]; }
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($chromosomes, $parents, $crossover) = ($ga->chromosomes, $ga->_parents, $ga->crossover);
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
my @children;
#-------------------------------------------------------------------
while(my $elders = shift @$parents){
my @elders = unpack 'I*', $elders;
unless(scalar @elders){
$_fitness->{scalar(@children)} = $fitness->($ga, $chromosomes->[$elders[0]]);
push @children, $chromosomes->[$elders[0]];
next;
}
my ($min, $max) = (0, $#{$chromosomes->[0]});
if($ga->variable_length){
lib/AI/Genetic/Pro/Crossover/PointsAdvanced.pm view on Meta::CPAN
use strict;
use List::MoreUtils qw(first_index);
#use Data::Dumper; $Data::Dumper::Sortkeys = 1;
#use AI::Genetic::Pro::Array::PackTemplate;
#=======================================================================
sub new { bless { points => $_[1] ? $_[1] : 1 }, $_[0]; }
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($chromosomes, $parents, $crossover) = ($ga->chromosomes, $ga->_parents, $ga->crossover);
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
#-------------------------------------------------------------------
while(my $elders = shift @$parents){
my @elders = unpack 'I*', $elders;
unless(scalar @elders){
push @$chromosomes, $chromosomes->[$elders[0]];
next;
}
my ($min, $max) = (0, $#{$chromosomes->[0]} - 1);
if($ga->variable_length){
for my $el(@elders){
lib/AI/Genetic/Pro/Crossover/PointsBasic.pm view on Meta::CPAN
use warnings;
use strict;
use List::MoreUtils qw(first_index);
#use Data::Dumper; $Data::Dumper::Sortkeys = 1;
#=======================================================================
sub new { bless { points => $_[1] ? $_[1] : 1 }, $_[0]; }
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($chromosomes, $parents, $crossover) = ($ga->chromosomes, $ga->_parents, $ga->crossover);
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
my @children;
#-------------------------------------------------------------------
while(my $elders = shift @$parents){
my @elders = unpack 'I*', $elders;
unless(scalar @elders){
$_fitness->{scalar(@children)} = $fitness->($ga, $chromosomes->[$elders[0]]);
push @children, $chromosomes->[$elders[0]];
next;
}
my ($min, $max) = (0, $#{$chromosomes->[0]} - 1);
if($ga->variable_length){
lib/AI/Genetic/Pro/Crossover/PointsSimple.pm view on Meta::CPAN
use warnings;
use strict;
use List::MoreUtils qw(first_index);
#use Data::Dumper; $Data::Dumper::Sortkeys = 1;
#=======================================================================
sub new { bless { points => $_[1] ? $_[1] : 1 }, $_[0]; }
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($chromosomes, $parents, $crossover) = ($ga->chromosomes, $ga->_parents, $ga->crossover);
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
my @children;
#-------------------------------------------------------------------
while(my $elders = shift @$parents){
my @elders = unpack 'I*', $elders;
unless(scalar @elders){
push @children, $chromosomes->[$elders[0]];
next;
}
my ($min, $max) = (0, $#{$chromosomes->[0]} - 1);
if($ga->variable_length){
for my $el(@elders){
lib/AI/Genetic/Pro/Selection/Distribution.pm view on Meta::CPAN
bless {
type => $type,
params => \@params,
}, $class;
}
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($fitness, $chromosomes) = ($ga->_fitness, $ga->chromosomes);
croak "You must set a number of parents to use the Distribution strategy"
unless defined($ga->parents);
my $parents = $ga->parents;
my @parents;
my $high = scalar @$chromosomes;
#-------------------------------------------------------------------
if($self->{type} eq q/uniform/){
push @parents,
pack 'I*', random_uniform_integer($parents, 0, $#$chromosomes)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/normal/){
my $av = defined $self->{params}->[0] ? $self->{params}->[0] : $#$chromosomes/2;
my $sd = defined $self->{params}->[1] ? $self->{params}->[1] : $#$chromosomes;
push @parents,
pack 'I*', map { int $_ % $high } random_normal($parents, $av, $sd)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/beta/){
my $aa = defined $self->{params}->[0] ? $self->{params}->[0] : $parents;
my $bb = defined $self->{params}->[1] ? $self->{params}->[1] : $parents;
push @parents,
pack 'I*', map { int($_ * $high) } random_beta($parents, $aa, $bb)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/binomial/){
push @parents,
pack 'I*', random_binomial($parents, $#$chromosomes, rand)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/chi_square/){
my $df = defined $self->{params}->[0] ? $self->{params}->[0] : $#$chromosomes;
push @parents,
pack 'I*', map { int $_ % $high } random_chi_square($parents, $df)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/exponential/){
my $av = defined $self->{params}->[0] ? $self->{params}->[0] : $#$chromosomes/2;
push @parents,
pack 'I*', map { int $_ % $high } random_exponential($parents, $av)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/poisson/){
my $mu = defined $self->{params}->[0] ? $self->{params}->[0] : $#$chromosomes/2;
push @parents,
pack 'I*', map { int $_ % $high } random_poisson($parents, $mu)
for 0..$#$chromosomes;
}else{
die qq/Unknown distribution "$self->{type}" in "selection"!\n/;
}
#-------------------------------------------------------------------
return \@parents;
}
#=======================================================================
1;
lib/AI/Genetic/Pro/Selection/Roulette.pm view on Meta::CPAN
use List::MoreUtils qw(first_index);
use Carp 'croak';
#=======================================================================
sub new { bless \$_[0], $_[0]; }
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($fitness) = ($ga->_fitness);
my (@parents, @elders);
#-------------------------------------------------------------------
my $count = $#{$ga->chromosomes};
my $const = min values %$fitness;
$const = $const < 0 ? abs($const) : 0;
my $total = sum( map { $_ < 0 ? $_ + $const : $_ } values %$fitness);
$total ||= 1;
# elders
for my $idx (0..$count){
push @elders, $idx for 1..int((($fitness->{$idx} + $const) / $total) * $count);
}
if((my $add = $count - scalar @elders) > 0){
my $idx = $elders[rand @elders];
push @elders, int rand($count) for 0..$add;
}
croak "You must set a crossover probability to use the Roulette strategy"
unless defined($ga->crossover);
croak "You must set a number of parents to use the Roulette strategy"
unless defined($ga->parents);
# parents
for(0..$count){
if(rand > $ga->crossover){
push @parents, pack 'I*', $elders[ rand @elders ]
}else{
my @group;
push @group, $elders[ rand @elders ] for 1..$ga->parents;
push @parents, pack 'I*', @group;
}
}
#-------------------------------------------------------------------
return \@parents;
}
#=======================================================================
1;
lib/AI/Genetic/Pro/Selection/RouletteBasic.pm view on Meta::CPAN
#use Data::Dumper; $Data::Dumper::Sortkeys = 1;
use List::MoreUtils qw(first_index);
use Carp 'croak';
#=======================================================================
sub new { bless \$_[0], $_[0]; }
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($fitness, $chromosomes) = ($ga->_fitness, $ga->chromosomes);
croak "You must set a number of parents to use the RouletteBasic strategy"
unless defined($ga->parents);
my $parents = $ga->parents;
my (@parents, @wheel);
my $const = min values %$fitness;
$const = $const < 0 ? abs($const) : 0;
my $total = 0;
#-------------------------------------------------------------------
foreach my $key (keys %$fitness){
$total += $fitness->{$key} + $const;
push @wheel, [ $key, $total ];
}
for(0..$#$chromosomes){
my @group;
for(1..$parents){
my $rand = rand($total);
my $idx = first_index { $_->[1] > $rand } @wheel;
if($idx == 0){ $idx = 1 }
elsif($idx == -1 ) { $idx = scalar @wheel; }
push @group, $wheel[$idx-1]->[0];
}
push @parents, pack 'I*', @group;
}
#-------------------------------------------------------------------
return \@parents;
}
#=======================================================================
1;
lib/AI/Genetic/Pro/Selection/RouletteDistribution.pm view on Meta::CPAN
my $idx = first_index { $_->[1] > $rand } @$wheel;
if($idx == 0){ $idx = 1 }
elsif($idx == -1 ) { $idx = scalar @$wheel; }
return $wheel->[$idx-1]->[0];
}
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($fitness, $chromosomes) = ($ga->_fitness, $ga->chromosomes);
croak "You must set a number of parents for the RouletteDistribution strategy"
unless defined($ga->parents);
my $parents = $ga->parents;
my $high = scalar @$chromosomes;
my (@parents, @wheel);
my $const = min values %$fitness;
$const = $const < 0 ? abs($const) : 0;
my $total = 0;
#-------------------------------------------------------------------
foreach my $key (keys %$fitness){
$total += $fitness->{$key} + $const;
push @wheel, [ $key, $total ];
}
#-------------------------------------------------------------------
if($self->{type} eq q/uniform/){
push @parents,
pack 'I*',
map { roulette($total, \@wheel) }
random_uniform($parents, 0, $total)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/normal/){
my $av = defined $self->{params}->[0] ? $self->{params}->[0] : $#$chromosomes/2;
my $sd = defined $self->{params}->[1] ? $self->{params}->[1] : $#$chromosomes;
push @parents,
pack 'I*',
map { roulette($total, \@wheel) }
map { int $_ % $high } random_normal($parents, $av, $sd)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/beta/){
my $aa = defined $self->{params}->[0] ? $self->{params}->[0] : $parents;
my $bb = defined $self->{params}->[1] ? $self->{params}->[1] : $parents;
push @parents,
pack 'I*',
map { roulette($total, \@wheel) }
map { int($_ * $high) } random_beta($parents, $aa, $bb)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/binomial/){
push @parents,
pack 'I*',
map { roulette($total, \@wheel) }
random_binomial($parents, $#$chromosomes, rand)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/chi_square/){
my $df = defined $self->{params}->[0] ? $self->{params}->[0] : $#$chromosomes;
push @parents,
pack 'I*',
map { roulette($total, \@wheel) }
map { int $_ % $high } random_chi_square($parents, $df)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/exponential/){
my $av = defined $self->{params}->[0] ? $self->{params}->[0] : $#$chromosomes/2;
push @parents,
pack 'I*',
map { roulette($total, \@wheel) }
map { int $_ % $high } random_exponential($parents, $av)
for 0..$#$chromosomes;
}elsif($self->{type} eq q/poisson/){
my $mu = defined $self->{params}->[0] ? $self->{params}->[0] : $#$chromosomes/2;
push @parents,
pack 'I*',
map { roulette($total, \@wheel) }
map { int $_ % $high } random_poisson($parents, $mu)
for 0..$#$chromosomes;
}else{
die qq/Unknown distribution "$self->{type}" in "selection"!\n/;
}
#-------------------------------------------------------------------
return \@parents;
}
#=======================================================================
1;
t/01_inject.t view on Meta::CPAN
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 => 0, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
t/02_cache.t view on Meta::CPAN
return sum(scalar $ga->as_array($chromosome));
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => sub { return; }, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 10, # 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 => 0, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
t/04_bitvectors_variable_length_I.t view on Meta::CPAN
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 => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
t/05_bitvectors_variable_length_II.t view on Meta::CPAN
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 => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
t/06_listvectors_constant_length.t view on Meta::CPAN
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # 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 => 0, # turn variable length OFF
);
my @data;
t/07_listvectors_variable_length_I.t view on Meta::CPAN
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # 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 => 1, # turn variable length OFF
);
my @data;
t/08_listvectors_variable_length_II.t view on Meta::CPAN
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.01, # 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;
t/09_rangevectors_constant_length.t view on Meta::CPAN
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 => 0, # turn variable length OFF
);
my @data;
t/10_rangevectors_variable_length_I.t view on Meta::CPAN
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 => 1, # turn variable length OFF
);
my @data;
t/11_rangevectors_variable_length_II.t view on Meta::CPAN
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;
t/12_combinations_constant_length.t view on Meta::CPAN
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'combination', # 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 => [ 'PMX' ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
$ga->init( [ 'a'..'h' ] );
t/13_preserve.t view on Meta::CPAN
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);
t/14_getFittest.t view on Meta::CPAN
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);
t/15_bitvectors_constant_length_MCE.t view on Meta::CPAN
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 => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
-mce => 1, # turn ON Many-Core Engine
);
# init population of 32-bit vectors
t/16_bitvectors_constant_length_-_native_arrays.t view on Meta::CPAN
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 => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
-native => 1, # turn ON use of native arrays
);
# init population of 32-bit vectors
t/17_bitvectors_constant_length_MCE_-_native_arrays.t view on Meta::CPAN
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 => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
-mce => 1, # turn ON Many-Core Engine
-native => 1, # turn ON use of native arrays
);
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