map results from the CPAN

AI-Genetic-Pro

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71 use constant STORABLE   => 'Storable';
use constant GD                 => 'GD::Graph::linespoints'; 
#=======================================================================
my $_Cache = { };
my $_temp_chromosome;
#=======================================================================
sub new {
        my ( $class, %args ) = ( shift, @_ );
         
        #-------------------------------------------------------------------
        my %opts = map { if(ref $_){$_}else{ /^-?(.*)$/o; $1 }} @_;
        my $self = bless \%opts, $class;
         
        #-------------------------------------------------------------------
        $AI::Genetic::Pro::Array::Type::Native = 1 if $self->native;
         
        #-------------------------------------------------------------------
        croak(q/Type of chromosomes cannot be "combination" if "variable length" feature is active!/)
                if $self->type eq q/combination/ and $self->variable_length;
        croak(q/You must specify a crossover strategy with -strategy!/)
                unless defined ($self->strategy);

lib/AI/Genetic/Pro.pm view on Meta::CPAN

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253         $Storable::Eval = 1;
        #+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
        my ( $self ) = @_;
         
        my $clone = { 
                _selector       => undef,
                _strategist     => undef,
                _mutator        => undef,
        };
         
        $clone->{ chromosomes } = [ map { ${ tied( @$_ ) } } @{ $self->chromosomes } ] 
                if $self->_package;
         
        foreach my $key(keys %$self){
                next if exists $clone->{$key};
                $clone->{$key} = $self->{$key};
        }
         
        return $clone;
}
#=======================================================================
sub slurp {
        my ( $self, $dump ) = @_;
 
        if( my $typ = $self->_package ){ 
                @{ $dump->{ chromosomes } } = map {
                        my $arr = $typ->make_with_packed( $_ );
                        bless $arr, q[AI::Genetic::Pro::Chromosome];
                } @{ $dump->{ chromosomes } };
        }
     
    %$self = %$dump;
     
        return 1;
}
#=======================================================================

lib/AI/Genetic/Pro.pm view on Meta::CPAN

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414                 #return @$chromosome if wantarray;
                #return $chromosome;
                 
                my @chr = @$chromosome;
                return @chr if wantarray;
                return \@chr;
                 
        }elsif($self->type eq q/rangevector/){
                my $fix_range = $self->_fix_range;
                my $c = -1;
                #my @array = map { $c++; warn "WARN: $c | ",scalar @$chromosome,"\n" if not defined $fix_range->[$c]; $_ ? $_ - $fix_range->[$c] : undef } @$chromosome;
                my @array = map { $c++; $_ ? $_ - $fix_range->[$c] : undef } @$chromosome;
                 
                return @array if wantarray;
                return \@array;
        }else{
                my $cnt = 0;
                my @array = map { $self->_translations->[$cnt++]->[$_] } @$chromosome;
                return @array if wantarray;
                return \@array;
        }
}
#=======================================================================
sub as_string_def_only {        
        my ($self, $chromosome) = @_;
         
        return $self->as_string($chromosome) 
                if not $self->variable_length or $self->variable_length < 2;
 
        my $array = $self->as_array_def_only($chromosome);
         
        return join(q//, @$array) if $self->type eq q/bitvector/;
        return join(q/___/, @$array);
}
#=======================================================================
sub as_string { 
        return join(q//, @{$_[1]}) if $_[0]->type eq q/bitvector/;
        return  join(q/___/, map { defined $_ ? $_ : q/ / } $_[0]->as_array($_[1]));
}
#=======================================================================
sub as_value { 
        my ($self, $chromosome) = @_;
        croak(q/You MUST call 'as_value' as method of 'AI::Genetic::Pro' object./)
                unless defined $_[0] and ref $_[0] and ( ref $_[0] eq 'AI::Genetic::Pro' or ref $_[0] eq 'AI::Genetic::Pro::MCE');
        croak(q/You MUST pass 'AI::Genetic::Pro::Chromosome' object to 'as_value' method./) 
                unless defined $_[1] and ref $_[1] and ref $_[1] eq 'AI::Genetic::Pro::Chromosome';
        return $self->fitness->($self, $chromosome);  
}

lib/AI/Genetic/Pro.pm view on Meta::CPAN

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        return 1;
}
#=======================================================================
sub _state {
        my ( $self ) = @_;
         
        my @res;
         
        if( $self->_package ){
                @res = map { 
                        [
                                ${ tied( @{ $self->chromosomes->[ $_ ] } ) },
                                $self->_fitness->{ $_ },
                        ]
                } 0 .. $self->population - 1
        }else{
                @res = map { 
                        [
                                $self->chromosomes->[ $_ ],
                                $self->_fitness->{ $_ },
                        ]
                } 0 .. $self->population - 1
        }
         
        return \@res;
}
#=======================================================================

lib/AI/Genetic/Pro.pm view on Meta::CPAN

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590 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] = $_;

lib/AI/Genetic/Pro/Chromosome.pm view on Meta::CPAN

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35 #use Math::Random qw(random_uniform_integer);
#=======================================================================
sub new {
        my ($class, $data, $type, $package, $length) = @_;
 
        my @genes;      
        tie @genes, $package if $package;
         
        if($type eq q/bitvector/){
                #@genes = random_uniform_integer(scalar @$data, 0, 1);                  # this is fastest, but uses more memory
                @genes = map { rand > 0.5 ? 1 : 0 } 0..$length;                                      # this is faster
                #@genes =  split(q//, unpack("b*", rand 99999), $#$data + 1);   # slow
        }elsif($type eq q/combination/){ 
                #@genes = shuffle 0..$#{$data->[0]}; 
                @genes = shuffle 0..$length; 
        }elsif($type eq q/rangevector/){
                @genes = map { $_->[1] + int rand($_->[2] - $_->[1] + 1) } @$data[0..$length];
        }else{ 
                @genes = map { 1 + int(rand( $#{ $data->[$_] })) } 0..$length; 
        }
 
        return bless \@genes, $class;
}
#=======================================================================
sub new_from_data {
        my ($class, $data, $type, $package, $values, $fix_range) = @_;
 
        die qq/\nToo many elements in the injected chromosome of type "$type": @$values\n/ if $#$values > $#$data;

lib/AI/Genetic/Pro/Crossover/Distribution.pm view on Meta::CPAN

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75 }
 
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
my $len = scalar @elders;
my @seq;
if($self->{type} eq q/uniform/){
        @seq = random_uniform_integer($high, 0, $#elders);
}elsif($self->{type} eq q/normal/){
        my $av = defined $self->{params}->[0] ? $self->{params}->[0] : $len/2;
        my $sd = defined $self->{params}->[1] ? $self->{params}->[1] : $len;
        @seq = map { $_ % $len } random_normal($high, $av, $sd);
}elsif($self->{type} eq q/beta/){
        my $aa = defined $self->{params}->[0] ? $self->{params}->[0] : $len;
        my $bb = defined $self->{params}->[1] ? $self->{params}->[1] : $len;
        @seq = map { int($_ * $len) } random_beta($high, $aa, $bb);
}elsif($self->{type} eq q/binomial/){
        @seq = random_binomial($high, $#elders, rand);
}elsif($self->{type} eq q/chi_square/){
        my $df = defined $self->{params}->[0] ? $self->{params}->[0] : $len;
        @seq = map { $_ % $len } random_chi_square($high, $df);
}elsif($self->{type} eq q/exponential/){
        my $av = defined $self->{params}->[0] ? $self->{params}->[0] : $len/2;
        @seq = map { $_ % $len } random_exponential($high, $av);
}elsif($self->{type} eq q/poisson/){
        my $mu = defined $self->{params}->[0] ? $self->{params}->[0] : $len/2;
        @seq = map { $_ % $len } random_poisson($high, $mu) ;
}else{
        die qq/Unknown distribution "$self->{type}" in "crossover"!\n/;
}
 
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
my ($min, $max) = (0, $#{$chromosomes->[0]} - 1);
if($ga->variable_length){
        for my $el(@elders){
                my $idx = first_index { $_ } @{$chromosomes->[$el]};
                $min = $idx if $idx > $min;

lib/AI/Genetic/Pro/Crossover/OX.pm view on Meta::CPAN

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63         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 {
                                        my @av = @{$a}[$points[0]..$points[1]];
                                        my @bv = @{$b}[$points[0]..$points[1]];
                                         
                                        for my $e(@av){
                                                splice(@$b, (first_index { $_ == $e } @$b), 1);
                                        }
                                        splice @$b, $points[0], 0, @av;
                                         
                                        for my $e(@bv){
                                                splice(@$a, (first_index { $_ == $e } @$a), 1);
                                        }
                                        splice @$a, $points[0], 0, @bv;
                                        0;
                                         
                                                } map { 
                                                        $chromosomes->[$_]->clone;
                                                                } @elders;
                 
                 
                my %elders = map { $_ => $fitness->($ga, $elders[$_]) } 0..$#elders;
                my $max = (sort { $elders{$a} <=> $elders{$b} } keys %elders)[-1];
                $_fitness->{scalar(@children)} = $elders{$max};
                 
                push @children, $elders[$max];
        }
        #-------------------------------------------------------------------
         
        return \@children;
}
#=======================================================================

lib/AI/Genetic/Pro/Crossover/PMX.pm view on Meta::CPAN

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44 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 {
                                my @av = @{$a}[$points[0]..$points[1]-1];
                                my @bv = splice @$b, $points[0], $points[1] - $points[0], @av;
                                splice @$a, $points[0], $points[1] - $points[0], @bv;
                                 
                                my %av; @av{@av} = @bv;
                                my %bv; @bv{@bv} = @av;
 
                                while(my $dup = dup($a)){

lib/AI/Genetic/Pro/Crossover/PMX.pm view on Meta::CPAN

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74                                         }
 
                                        while(my $dup = dup($b)){
                                        foreach my $val (@$dup){
                                                my ($ind) = grep { $_ < $points[0] or $_ >= $points[1] } indexes { $_ == $val } @$b;
                                                $b->[$ind] = $av{$val};
                                        }
                                        }
                                         
                                        0;
                                                } map { 
                                                        $chromosomes->[$_]->clone
                                                                } @elders;
                 
                 
                my %elders = map { $_ => $fitness->($ga, $elders[$_]) } 0..$#elders;
                my $max = (sort { $elders{$a} <=> $elders{$b} } keys %elders)[-1];
                $_fitness->{scalar(@children)} = $elders{$max};
                 
                push @children, $elders[$max];
        }
        #-------------------------------------------------------------------
         
        return \@children;
}
#=======================================================================

lib/AI/Genetic/Pro/Crossover/Points.pm view on Meta::CPAN

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60                         for my $el(@elders){
                                my $idx = first_index { $_ } @{$chromosomes->[$el]};
                                $min = $idx if $idx > $min;
                                $max = $#{$chromosomes->[$el]} if $#{$chromosomes->[$el]} < $max;
                        }
                }
                 
                my @points;
                if($min < $max and $max - $min > 2){
                        my $range = $max - $min;
                        @points = map { $min + int(rand $range) } 1..$self->{points};
                }
 
                @elders = map { $chromosomes->[$_]->clone } @elders;
                 
                for my $pt(@points){
                        @elders = sort {
                                                splice @$b, 0, $pt, splice( @$a, 0, $pt, @$b[0..$pt-1] );
                                                0;
                                                        } @elders;
                }
                 
                my %elders = map { $_ => $fitness->($ga, $elders[$_]) } 0..$#elders;
                my $maximum = (sort { $elders{$a} <=> $elders{$b} } keys %elders)[-1];
                $_fitness->{scalar(@children)} = $elders{$maximum};
                 
                push @children, $elders[$maximum];
        }
        #-------------------------------------------------------------------
        return \@children;
}
#=======================================================================
1;

lib/AI/Genetic/Pro/Crossover/PointsAdvanced.pm view on Meta::CPAN

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59                         for my $el(@elders){
                                my $idx = first_index { $_ } @{$chromosomes->[$el]};
                                $min = $idx if $idx > $min;
                                $max = $#{$chromosomes->[$el]} if $#{$chromosomes->[$el]} < $max;
                        }
                }
                 
                my @points;
                if($min < $max and $max - $min > 2){
                        my $range = $max - $min;
                        @points = map { $min + int(rand $range) } 1..$self->{points};
                }
 
                @elders = map { $chromosomes->[$_]->clone } @elders;
                for my $pt(@points){
                        @elders = sort {
                                                splice @$b, 0, $pt, splice( @$a, 0, $pt, @$b[0..$pt-1] );
                                                0;
                                                        } @elders;
                }
                 
                push @$chromosomes, @elders;
        }
        #-------------------------------------------------------------------
        # wybieranie potomkow ze zbioru starych i nowych osobnikow
        @$chromosomes = sort { $fitness->($ga, $a) <=> $fitness->($ga, $b) } @$chromosomes;
        splice @$chromosomes, 0, scalar(@$chromosomes) - $ga->population;
        %$_fitness = map { $_ => $fitness->($ga, $chromosomes->[$_]) } 0..$#$chromosomes;
        #-------------------------------------------------------------------
        return $chromosomes;
}
#=======================================================================
1;

lib/AI/Genetic/Pro/Crossover/PointsBasic.pm view on Meta::CPAN

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51         for my $el(@elders){
                my $idx = first_index { $_ } @{$chromosomes->[$el]};
                $min = $idx if $idx > $min;
                $max = $#{$chromosomes->[$el]} if $#{$chromosomes->[$el]} < $max;
        }
}
 
my @points;
if($min < $max and $max - $min > 2){
        my $range = $max - $min;
        @points = map { $min + int(rand $range) } 1..$self->{points};
}
 
@elders = map { $chromosomes->[$_]->clone } @elders;
for my $pt(@points){
        @elders = sort {
                                splice @$b, 0, $pt, splice( @$a, 0, $pt, @$b[0..$pt-1] );
                                0;
                                        } @elders;
}
 
my $idx = int rand @elders;
$_fitness->{scalar(@children)} = $fitness->($ga, $elders[$idx]);
push @children, $elders[ $idx ];

lib/AI/Genetic/Pro/Crossover/PointsSimple.pm view on Meta::CPAN

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59                         for my $el(@elders){
                                my $idx = first_index { $_ } @{$chromosomes->[$el]};
                                $min = $idx if $idx > $min;
                                $max = $#{$chromosomes->[$el]} if $#{$chromosomes->[$el]} < $max;
                        }
                }
                 
                my @points;
                if($min < $max and $max - $min > 2){
                        my $range = $max - $min;
                        @points = map { $min + int(rand $range) } 1..$self->{points};
                }
                 
                @elders = map { $chromosomes->[$_]->clone } @elders;
                for my $pt(@points){
                        @elders = sort {
                                                splice @$b, 0, $pt, splice( @$a, 0, $pt, @$b[0..$pt-1] );
                                                0;
                                                        } @elders;
                }
                 
                push @children, @elders;
        }
        #-------------------------------------------------------------------
        # wybieranie potomkow ze zbioru nowych osobnikow
        @children = sort { $fitness->($ga, $a) <=> $fitness->($ga, $b) } @children;
        splice @children, 0, scalar(@children) - scalar(@$chromosomes);
        %$_fitness = map { $_ => $fitness->($ga, $children[$_]) } 0..$#children;
        #-------------------------------------------------------------------
        return \@children;
}
#=======================================================================
1;

lib/AI/Genetic/Pro/MCE.pm view on Meta::CPAN

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73         my @pop = ( $pop ) x $self->workers;
        $pop[ 0 ] += $rst;
         
        $self->_pop( \@pop );
}
#=======================================================================
sub _calculate_fitness_all {
        my ($self) = @_;
         
        # Faster version. Thanks to Mario Roy :-)
        my %fit = mce_map_s {
                        $_ => $self->fitness()->( $self, $self->chromosomes->[ $_ ] )
                } 0, $#{ $self->chromosomes };
 
        # The old one
        #my %fit = mce_map {
        #               $_ => $self->fitness()->( $self, $self->chromosomes->[ $_ ] )
        #       } 0 .. $#{ $self->chromosomes };
 
        $self->_fitness( \%fit );
         
        return;
}
#=======================================================================
sub _init_mce {
        my ( $self ) = @_;

lib/AI/Genetic/Pro/MCE.pm view on Meta::CPAN

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113 #-------------------------------------------------------------------
my $pop = $self->population;
$self->population( 1 );
$self->SUPER::init(  $val  );
$self->population( $pop );
 
#-------------------------------------------------------------------
my $one = shift @{ $self->chromosomes };     
my $tpl = $self->_tpl;
 
my @lst = mce_map {
        my $arg = clone( $tpl );
        $arg->{ -population } = $_;
        my $gal = AI::Genetic::Pro->new( %$arg );
        $gal->init( $val );
        @{ $gal->_state };
         
} @{ $self->_pop };
 
#-------------------------------------------------------------------
return $self->_adopt( \@lst );

lib/AI/Genetic/Pro/MCE.pm view on Meta::CPAN

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171 }
#=======================================================================
sub _chunks {
        my ( $self ) = @_;
         
        my $cnt = 0;
        my @chk;
         
        for my $idx ( 0 .. $#{ $self->_pop } ){
                my $pos = 0;
                my %tmp = map { $pos++ => $self->_fitness->{ $_ } } $cnt .. $cnt + $self->_pop->[ $idx ] -1 ;
                my @tmp = splice @{ $self->chromosomes }, 0, $self->_pop->[ $idx ];
                $cnt += @tmp;
                 
                if( $self->_package ){
                        push @chk, [
                                [ map { ${ tied( @$_ ) } } @tmp ],
                                \%tmp,
                        ];
                }else{
                        push @chk, [
                                \@tmp,
                                \%tmp,
                        ];
                }
        }

lib/AI/Genetic/Pro/MCE.pm view on Meta::CPAN

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202 # terminate ----------------------------------------------------
last if $self->terminate and $self->terminate->( $self );
 
# update generation --------------------------------------------
$self->generation($self->generation + 1);
 
# update history -----------------------------------------------
$self->_save_history;
 
my $tpl = $self->_tpl;
my @lst = mce_map {
        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        my $ary = $_;
        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        my $arg = clone( $tpl );
        $arg->{ -population } = 1;
        my $gal = AI::Genetic::Pro->new( %$arg );
        $gal->init( 1 );
        #- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
        if( my $typ = $self->_package ){
                for my $idx ( 0 .. $#{ $ary->[ 0 ] } ){

lib/AI/Genetic/Pro/Selection/Distribution.pm view on Meta::CPAN

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78         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;
}
#=======================================================================

lib/AI/Genetic/Pro/Selection/Roulette.pm view on Meta::CPAN

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32 #=======================================================================
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;

lib/AI/Genetic/Pro/Selection/RouletteDistribution.pm view on Meta::CPAN

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111         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;
}
#=======================================================================

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