AI-Genetic-Pro
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return $result == 4294967295 ? 1 : 0;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 1000, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.01, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 1, # remember best results
-preserve => 3, # remember the bests
-variable_length => 1, # turn variable length ON
-mce => 1, # optional MCE support
-workers => 3, # number of workers (MCE)
);
# where 'x' means 'undef'
# ...and so on
In this situation returned chromosomes in an array context
($ga->as_array($chromosome)) can have undef values on the left
side (only). In a scalar context each undefined value is replaced
with a single space. If You don't want to see any undef or space,
just use as_array_def_only and as_string_def_only instead of
as_array and as_string.
-parents
This defines how many parents should be used in a crossover.
-selection
This defines how individuals/chromosomes are selected to crossover.
It expects an array reference listed below:
-selection => [ $type, @params ]
where type is one of:
RouletteBasic
Each individual/chromosome can be selected with probability
proportional to its fitness.
Roulette
First the best individuals/chromosomes are selected. From this
collection parents are selected with probability poportional to
their fitness.
RouletteDistribution
Each individual/chromosome has a portion of roulette wheel
proportional to its fitness. Selection is done with the specified
distribution. Supported distributions and parameters are listed
below.
-selection => [ 'RouletteDistribution', 'uniform' ]
Normal distribution, where $av is average (default: size of
population /2) and $$sd is standard deviation (default: size of
population).
-selection => [ 'RouletteDistribution', 'beta', $aa, $bb ]
Beta distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
$aa and $bb are set by default to number of parents.
Argument restrictions: Both $aa and $bb must not be less than
1.0E-37.
-selection => [ 'RouletteDistribution', 'binomial' ]
Binomial distribution. No additional parameters are needed.
-selection => [ 'RouletteDistribution', 'chi_square', $df ]
Normal distribution, where $av is average (default: size of
population /2) and $$sd is standard deviation (default: size of
population).
-selection => [ 'Distribution', 'beta', $aa, $bb ]
Beta distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
$aa and $bb are set by default to number of parents.
Argument restrictions: Both $aa and $bb must not be less than
1.0E-37.
-selection => [ 'Distribution', 'binomial' ]
Binomial distribution. No additional parameters are needed.
-selection => [ 'Distribution', 'chi_square', $df ]
chromosomes/individuals are selected for the new generation. For
example:
-strategy => [ 'PointsSimple', $n ]
where $n is the number of points for crossing.
PointsBasic
Crossover in one or many points. In basic crossover selected
parents are crossed and one (randomly-chosen) child is moved to
the new generation. For example:
-strategy => [ 'PointsBasic', $n ]
where $n is the number of points for crossing.
Points
Crossover in one or many points. In normal crossover selected
parents are crossed and the best child is moved to the new
generation. For example:
-strategy => [ 'Points', $n ]
where $n is number of points for crossing.
PointsAdvenced
Crossover in one or many points. After crossover the best
chromosomes/individuals from all parents and chidren are selected
for the new generation. For example:
-strategy => [ 'PointsAdvanced', $n ]
where $n is the number of points for crossing.
Distribution
In distribution crossover parents are crossed in points selected
with the specified distribution. See below.
-strategy => [ 'Distribution', 'uniform' ]
Standard uniform distribution. No additional parameters are
needed.
-strategy => [ 'Distribution', 'normal', $av, $sd ]
Normal distribution, where $av is average (default: number of
parents/2) and $sd is standard deviation (default: number of
parents).
-strategy => [ 'Distribution', 'beta', $aa, $bb ]
Beta distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
$aa and $bb are set by default to the number of parents.
Argument restrictions: Both $aa and $bb must not be less than
1.0E-37.
-strategy => [ 'Distribution', 'binomial' ]
Binomial distribution. No additional parameters are needed.
-strategy => [ 'Distribution', 'chi_square', $df ]
Chi-squared distribution with $df degrees of freedom. $df by
default is set to the number of parents.
-strategy => [ 'Distribution', 'exponential', $av ]
Exponential distribution, where $av is average . $av by default
is set to the number of parents.
-strategy => [ 'Distribution', 'poisson', $mu ]
Poisson distribution, where $mu is mean. $mu by default is set
to the number of parents.
PMX
PMX method defined by Goldberg and Lingle in 1985. Parameters:
none.
OX
OX method defined by Davis (?) in 1985. Parameters: none.
Alias for crossProb.
$ga->mutProb()
This method is used to query and set the mutation rate.
$ga->mutation()
Alias for mutProb.
$ga->parents($parents)
Set/get number of parents in a crossover.
$ga->init($args)
This method initializes the population with random
individuals/chromosomes. It MUST be called before any call to
evolve(). It expects one argument, which depends on the type of
individuals/chromosomes:
bitvector
lib/AI/Genetic/Pro.pm view on Meta::CPAN
use AI::Genetic::Pro::Chromosome;
#-----------------------------------------------------------------------
__PACKAGE__->mk_accessors(qw(
mce
type
population
terminate
chromosomes
crossover
native
parents _parents
history _history
fitness _fitness _fitness_real
cache
mutation _mutator
strategy _strategist
selection _selector
_translations
generation
preserve
variable_length
lib/AI/Genetic/Pro.pm view on Meta::CPAN
values_vertical => 1,
values_format => ($params{-format} || '%.2f'),
zero_axis => 1,
#interlaced => 1,
logo_position => 'BR',
legend_placement => 'RT',
bgclr => 'white',
boxclr => '#FFFFAA',
transparent => 0,
title => ($params{'-title'} || q/Evolution/ ),
x_label => ($params{'-x_label'} || q/Generation/),
y_label => ($params{'-y_label'} || q/Value/ ),
( $params{-logo} && -f $params{-logo} ? ( logo => $params{-logo} ) : ( ) )
);
my $gd = $graph->plot( [ [ 0..$#{$data->[0]} ], @$data ] ) or croak($@);
lib/AI/Genetic/Pro.pm view on Meta::CPAN
# delete $self->_fitness->{$idx};
# delete $self->chromosomes->[$idx];
# }
#
# $self->_fitness(\%fitness);
# $self->chromosomes(\@chromosomes);
return;
}
#=======================================================================
sub _select_parents {
my ($self) = @_;
unless($self->_selector){
croak "You must specify a selection strategy!"
unless defined $self->selection;
my @tmp = @{$self->selection};
my $selector = q/AI::Genetic::Pro::Selection::/ . shift @tmp;
$selector->require or die $!;
$self->_selector($selector->new(@tmp));
}
$self->_parents($self->_selector->run($self));
return;
}
#=======================================================================
sub _crossover {
my ($self) = @_;
unless($self->_strategist){
my @tmp = @{$self->strategy};
my $strategist = q/AI::Genetic::Pro::Crossover::/ . shift @tmp;
lib/AI/Genetic/Pro.pm view on Meta::CPAN
# split into two loops just for speed
unless($self->preserve){
for(my $i = 0; $i != $generations; $i++){
# terminate ----------------------------------------------------
last if $self->terminate and $self->terminate->($self);
# update generation --------------------------------------------
$self->generation($self->generation + 1);
# update history -----------------------------------------------
$self->_save_history;
# selection ----------------------------------------------------
$self->_select_parents();
# crossover ----------------------------------------------------
$self->_crossover();
# mutation -----------------------------------------------------
$self->_mutation();
}
}else{
croak('You cannot preserve more chromosomes than is in population!') if $self->preserve > $self->population;
my @preserved;
for(my $i = 0; $i != $generations; $i++){
# terminate ----------------------------------------------------
last if $self->terminate and $self->terminate->($self);
# update generation --------------------------------------------
$self->generation($self->generation + 1);
# update history -----------------------------------------------
$self->_save_history;
#---------------------------------------------------------------
# preservation of N unique chromosomes
@preserved = map { clone($_) } @{ $self->getFittest_as_arrayref($self->preserve - 1, 1) };
# selection ----------------------------------------------------
$self->_select_parents();
# crossover ----------------------------------------------------
$self->_crossover();
# mutation -----------------------------------------------------
$self->_mutation();
#---------------------------------------------------------------
for(@preserved){
my $idx = int rand @{$self->chromosomes};
$self->chromosomes->[$idx] = $_;
$self->_fitness->{$idx} = $self->fitness()->($self, $_);
}
lib/AI/Genetic/Pro.pm view on Meta::CPAN
return $result == 4294967295 ? 1 : 0;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 1000, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.01, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 1, # remember best results
-preserve => 3, # remember the bests
-variable_length => 1, # turn variable length ON
-mce => 1, # optional MCE support
-workers => 3, # number of workers (MCE)
);
lib/AI/Genetic/Pro.pm view on Meta::CPAN
# ...and so on
In this situation returned chromosomes in an array context ($ga-E<gt>as_array($chromosome))
can have B<undef> values on the left side (only). In a scalar context each
undefined value is replaced with a single space. If You don't want to see
any C<undef> or space, just use C<as_array_def_only> and C<as_string_def_only>
instead of C<as_array> and C<as_string>.
=back
=item -parents
This defines how many parents should be used in a crossover.
=item -selection
This defines how individuals/chromosomes are selected to crossover. It expects an array reference listed below:
-selection => [ $type, @params ]
where type is one of:
=over 8
=item B<RouletteBasic>
Each individual/chromosome can be selected with probability proportional to its fitness.
=item B<Roulette>
First the best individuals/chromosomes are selected. From this collection
parents are selected with probability poportional to their fitness.
=item B<RouletteDistribution>
Each individual/chromosome has a portion of roulette wheel proportional to its
fitness. Selection is done with the specified distribution. Supported
distributions and parameters are listed below.
=over 12
=item C<-selection =E<gt> [ 'RouletteDistribution', 'uniform' ]>
lib/AI/Genetic/Pro.pm view on Meta::CPAN
Normal distribution, where C<$av> is average (default: size of population /2) and $C<$sd> is standard deviation (default: size of population).
=item C<-selection =E<gt> [ 'RouletteDistribution', 'beta', $aa, $bb ]>
I<Beta> distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
C<$aa> and C<$bb> are set by default to number of parents.
B<Argument restrictions:> Both $aa and $bb must not be less than 1.0E-37.
=item C<-selection =E<gt> [ 'RouletteDistribution', 'binomial' ]>
Binomial distribution. No additional parameters are needed.
=item C<-selection =E<gt> [ 'RouletteDistribution', 'chi_square', $df ]>
Chi-square distribution with C<$df> degrees of freedom. C<$df> by default is set to size of population.
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=item C<-selection =E<gt> [ 'Distribution', 'normal', $av, $sd ]>
Normal distribution, where C<$av> is average (default: size of population /2) and $C<$sd> is standard deviation (default: size of population).
=item C<-selection =E<gt> [ 'Distribution', 'beta', $aa, $bb ]>
I<Beta> distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
C<$aa> and C<$bb> are set by default to number of parents.
B<Argument restrictions:> Both $aa and $bb must not be less than 1.0E-37.
=item C<-selection =E<gt> [ 'Distribution', 'binomial' ]>
Binomial distribution. No additional parameters are needed.
=item C<-selection =E<gt> [ 'Distribution', 'chi_square', $df ]>
Chi-square distribution with C<$df> degrees of freedom. C<$df> by default is set to size of population.
lib/AI/Genetic/Pro.pm view on Meta::CPAN
Simple crossover in one or many points. The best chromosomes/individuals are
selected for the new generation. For example:
-strategy => [ 'PointsSimple', $n ]
where C<$n> is the number of points for crossing.
=item PointsBasic
Crossover in one or many points. In basic crossover selected parents are
crossed and one (randomly-chosen) child is moved to the new generation. For
example:
-strategy => [ 'PointsBasic', $n ]
where C<$n> is the number of points for crossing.
=item Points
Crossover in one or many points. In normal crossover selected parents are crossed and the best child is moved to the new generation. For example:
-strategy => [ 'Points', $n ]
where C<$n> is number of points for crossing.
=item PointsAdvenced
Crossover in one or many points. After crossover the best
chromosomes/individuals from all parents and chidren are selected for the new
generation. For example:
-strategy => [ 'PointsAdvanced', $n ]
where C<$n> is the number of points for crossing.
=item Distribution
In I<distribution> crossover parents are crossed in points selected with the
specified distribution. See below.
=over 8
=item C<-strategy =E<gt> [ 'Distribution', 'uniform' ]>
Standard uniform distribution. No additional parameters are needed.
=item C<-strategy =E<gt> [ 'Distribution', 'normal', $av, $sd ]>
Normal distribution, where C<$av> is average (default: number of parents/2) and C<$sd> is standard deviation (default: number of parents).
=item C<-strategy =E<gt> [ 'Distribution', 'beta', $aa, $bb ]>
I<Beta> distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
C<$aa> and C<$bb> are set by default to the number of parents.
B<Argument restrictions:> Both $aa and $bb must not be less than 1.0E-37.
=item C<-strategy =E<gt> [ 'Distribution', 'binomial' ]>
Binomial distribution. No additional parameters are needed.
=item C<-strategy =E<gt> [ 'Distribution', 'chi_square', $df ]>
Chi-squared distribution with C<$df> degrees of freedom. C<$df> by default is set to the number of parents.
=item C<-strategy =E<gt> [ 'Distribution', 'exponential', $av ]>
Exponential distribution, where C<$av> is average . C<$av> by default is set to the number of parents.
=item C<-strategy =E<gt> [ 'Distribution', 'poisson', $mu ]>
Poisson distribution, where C<$mu> is mean. C<$mu> by default is set to the number of parents.
=back
=item PMX
PMX method defined by Goldberg and Lingle in 1985. Parameters: I<none>.
=item OX
OX method defined by Davis (?) in 1985. Parameters: I<none>.
lib/AI/Genetic/Pro.pm view on Meta::CPAN
Alias for C<crossProb>.
=item I<$ga>-E<gt>B<mutProb>()
This method is used to query and set the mutation rate.
=item I<$ga>-E<gt>B<mutation>()
Alias for C<mutProb>.
=item I<$ga>-E<gt>B<parents>($parents)
Set/get number of parents in a crossover.
=item I<$ga>-E<gt>B<init>($args)
This method initializes the population with random individuals/chromosomes. It MUST be called before any call to C<evolve()>. It expects one argument, which depends on the type of individuals/chromosomes:
=over 4
=item B<bitvector>
For bitvectors, the argument is simply the length of the bitvector.
lib/AI/Genetic/Pro/Crossover/Distribution.pm view on Meta::CPAN
my ($class, $type, @params) = @_;
bless {
type => $type,
params => \@params,
}, $class;
}
#=======================================================================
sub run {
my ($self, $ga) = @_;
my ($chromosomes, $parents, $crossover) = ($ga->chromosomes, $ga->_parents, $ga->crossover);
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
my $high = scalar @{$chromosomes->[0]};
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 $len = scalar @elders;
lib/AI/Genetic/Pro/Crossover/OX.pm view on Meta::CPAN
#=======================================================================
sub save_fitness {
my ($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/02_cache.t view on Meta::CPAN
my $time0 =Time::HiRes::tv_interval($start);
$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 => 1, # 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/03_bitvectors_constant_length.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 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
);
( run in 0.398 second using v1.01-cache-2.11-cpan-4d50c553e7e )