AI-Genetic-Pro
view release on metacpan or search on metacpan
"Struct::Compare" : "0",
"Tie::Array::Packed" : "0.13",
"UNIVERSAL::require" : "0"
}
}
},
"release_status" : "stable",
"resources" : {
"homepage" : "https://github.com/d-strzelec/AI-Genetic-Pro",
"repository" : {
"type" : "git",
"url" : "https://github.com/d-strzelec/AI-Genetic-Pro.git",
"web" : "https://github.com/d-strzelec/AI-Genetic-Pro"
}
},
"version" : "1.009",
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sub terminate {
my ($ga) = @_;
my $result = oct('0b' . $ga->as_string($ga->getFittest));
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
-fitness
This defines a fitness function. It expects a reference to a
subroutine.
-terminate
This defines a terminate function. It expects a reference to a
subroutine.
-type
This defines the type of chromosomes. Currently, AI::Genetic::Pro
supports four types:
bitvector
Individuals/chromosomes of this type have genes that are bits.
Each gene can be in one of two possible states, on or off.
listvector
Each gene of a "listvector" individual/chromosome can assume one
string value from a specified list of possible string values.
rangevector
Each gene of a "rangevector" individual/chromosome can assume one
# or
-preserve => 9, # 9 chromosomes will be preserved
# and so on...
Attention! You cannot preserve more chromosomes than exist in your
population.
-variable_length
This defines whether variable-length chromosomes are turned on
(default off) and a which types of mutation are allowed. See below.
level 0
Feature is inactive (default). Example:
-variable_length => 0
# chromosomes (i.e. bitvectors)
0 1 0 0 1 1 0 1 1 1 0 1 0 1
0 0 1 1 0 1 1 1 1 0 0 1 1 0
-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
-selection => [ 'Distribution', 'poisson', $mu ]
Poisson distribution, where $mu is mean. $mu by default is set
to size of population.
-strategy
This defines the astrategy of crossover operation. It expects an
array reference listed below:
-strategy => [ $type, @params ]
where type is one of:
PointsSimple
Simple crossover in one or many points. The best
chromosomes/individuals are selected for the new generation. For
example:
-strategy => [ 'PointsSimple', $n ]
where $n is the number of points for crossing.
}
$ga->population($population)
Set/get size of the population. This defines the size of the
population, i.e. how many chromosomes to simultaneously exist at each
generation.
$ga->indType()
Get type of individuals/chromosomes. Currently supported types are:
bitvector
Chromosomes will be just bitvectors. See documentation of new
method.
listvector
Chromosomes will be lists of specified values. See documentation of
new method.
documentation of new method.
combination
Chromosomes will be unique lists of specified values. This is used
for example in the Traveling Salesman Problem. See the
documentation of the new method.
In example:
my $type = $ga->type();
$ga->type()
Alias for indType.
$ga->crossProb()
This method is used to query and set the crossover rate.
$ga->crossover()
Alias for crossProb.
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
For bitvectors, the argument is simply the length of the bitvector.
$ga->init(10);
This initializes a population where each individual/chromosome has
10 genes.
$ga->as_array_def_only($chromosome)
In list context return an array representing the specified
chromosome. In scalar context return an reference to an array
representing the specified chromosome. If variable_length is turned
off, this function is just an alias for as_array. If variable_length
is turned on and is set to level 2, this function will return only
not undef values from chromosome. See example below:
# -variable_length => 2, -type => 'bitvector'
my @chromosome = $ga->as_array($chromosome)
# @chromosome looks something like that
# ( undef, undef, undef, 1, 0, 1, 1, 1, 0 )
@chromosome = $ga->as_array_def_only($chromosome)
# @chromosome looks something like that
# ( 1, 0, 1, 1, 1, 0 )
$ga->as_string($chromosome)
Return a string representation of the specified chromosome. See
example below:
# -type => 'bitvector'
my $string = $ga->as_string($chromosome);
# $string looks something like that
# 1___0___1___1___1___0
# or
# -type => 'listvector'
$string = $ga->as_string($chromosome);
# $string looks something like that
# element0___element1___element2___element3...
Attention! If variable_length is turned on and is set to level 2, it
is possible to get undef values on the left side of the vector. In
the returned string undef values will be replaced with spaces. If you
don't want to see any spaces, use as_string_def_only instead of
as_string.
$ga->as_string_def_only($chromosome)
Return a string representation of specified chromosome. If
variable_length is turned off, this function is just alias for
as_string. If variable_length is turned on and is set to level 2,
this function will return a string without undef values. See example
below:
# -variable_length => 2, -type => 'bitvector'
my $string = $ga->as_string($chromosome);
# $string looks something like that
# ___ ___ ___1___1___0
$string = $ga->as_string_def_only($chromosome);
# $string looks something like that
# 1___1___0
$ga->as_value($chromosome)
lib/AI/Genetic/Pro.pm view on Meta::CPAN
use List::MoreUtils qw( minmax first_index apply );
#use Data::Dumper; $Data::Dumper::Sortkeys = 1;
use Tie::Array::Packed;
use UNIVERSAL::require;
#-----------------------------------------------------------------------
use AI::Genetic::Pro::Array::Type qw( get_package_by_element_size );
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
lib/AI/Genetic/Pro.pm view on Meta::CPAN
#-------------------------------------------------------------------
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);
croak(q/Type of chromosomes cannot be "combination" if strategy is not one of: OX, PMX!/)
if $self->type eq q/combination/ and ($self->strategy->[0] ne q/OX/ and $self->strategy->[0] ne q/PMX/);
croak(q/Strategy cannot be "/,$self->strategy->[0],q/" if "variable length" feature is active!/ )
if ($self->strategy->[0] eq 'PMX' or $self->strategy->[0] eq 'OX') and $self->variable_length;
#-------------------------------------------------------------------
$self->_set_strict if $self->strict;
#-------------------------------------------------------------------
return $self unless $self->mce;
#-------------------------------------------------------------------
lib/AI/Genetic/Pro.pm view on Meta::CPAN
croak q/You have to pass some data to "init"!/ unless $data;
#-------------------------------------------------------------------
$self->generation(0);
$self->_init( $data );
$self->_fitness( { } );
$self->_fix_range( [ ] );
$self->_history( [ [ ], [ ], [ ] ] );
$self->_init_cache if $self->cache;
#-------------------------------------------------------------------
if($self->type eq q/listvector/){
croak(q/You have to pass array reference if "type" is set to "listvector"/) unless ref $data eq 'ARRAY';
$self->_translations( $self->_check_data_ref($data) );
}elsif($self->type eq q/bitvector/){
croak(q/You have to pass integer if "type" is set to "bitvector"/) if $data !~ /^\d+$/o;
$self->_translations( [ [ 0, 1 ] ] );
$self->_translations->[$_] = $self->_translations->[0] for 1..$data-1;
}elsif($self->type eq q/combination/){
croak(q/You have to pass array reference if "type" is set to "combination"/) unless ref $data eq 'ARRAY';
$self->_translations( [ clone($data) ] );
$self->_translations->[$_] = $self->_translations->[0] for 1..$#$data;
}elsif($self->type eq q/rangevector/){
croak(q/You have to pass array reference if "type" is set to "rangevector"/) unless ref $data eq 'ARRAY';
$self->_translations( $self->_find_fix_range( $self->_check_data_ref($data) ));
}else{
croak(q/You have to specify first "type" of vector!/);
}
my $size = 0;
if($self->type ne q/rangevector/){ for(@{$self->_translations}){ $size = $#$_ if $#$_ > $size; } }
# else{ for(@{$self->_translations}){ $size = $_->[1] if $_->[1] > $size; } }
else{ for(@{$self->_translations}){ $size = $_->[2] if $_->[2] > $size; } } # Provisional patch for rangevector values truncated to signed 8-bit quantities. Thx to Tod Hagan
my $package = get_package_by_element_size($size);
$self->_package($package);
my $length = ref $data ? sub { $#$data; } : sub { $data - 1 };
if($self->variable_length){
$length = ref $data ? sub { 1 + int( rand( $#{ $self->_init } ) ); } : sub { 1 + int( rand( $self->_init - 1) ); };
}
$self->_length( $length );
$self->chromosomes( [ ] );
push @{$self->chromosomes},
AI::Genetic::Pro::Chromosome->new($self->_translations, $self->type, $package, $length->())
for 1..$self->population;
$self->_calculate_fitness_all();
}
#=======================================================================
# SAVE / LOAD ##########################################################
#=======================================================================
sub spew {
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
STORABLE->use( qw( store retrieve freeze thaw ) ) or croak(q/You need "/.STORABLE.q/" module to save a state of "/.__PACKAGE__.q/"!/);
lib/AI/Genetic/Pro.pm view on Meta::CPAN
}
#=======================================================================
# TRANSLATIONS #########################################################
#=======================================================================
sub as_array_def_only {
my ($self, $chromosome) = @_;
return $self->as_array($chromosome)
if not $self->variable_length or $self->variable_length < 2;
if( $self->type eq q/bitvector/ ){
return $self->as_array($chromosome);
}else{
my $ar = $self->as_array($chromosome);
my $idx = first_index { $_ } @$ar;
my @array = @$ar[$idx..$#$chromosome];
return @array if wantarray;
return \@array;
}
}
#=======================================================================
sub as_array {
my ($self, $chromosome) = @_;
if($self->type eq q/bitvector/){
# This could lead to internal error, bacause of underlaying Tie::Array::Packed
#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;
lib/AI/Genetic/Pro.pm view on Meta::CPAN
}
#=======================================================================
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
my $a = $self->_strategist->run($self);
$self->chromosomes( $a );
return;
}
#=======================================================================
sub _mutation {
my ($self) = @_;
unless($self->_mutator){
my $mutator = q/AI::Genetic::Pro::Mutation::/ . ucfirst(lc($self->type));
unless($mutator->require){
$mutator = q/AI::Genetic::Pro::Mutation::Listvector/;
$mutator->require;
}
$self->_mutator($mutator->new);
}
return $self->_mutator->run($self);
}
#=======================================================================
lib/AI/Genetic/Pro.pm view on Meta::CPAN
push @{$_[0]->_history->[1]}, $tmp[1];
push @{$_[0]->_history->[2]}, $tmp[2];
return 1;
}
#=======================================================================
sub inject {
my ($self, $candidates) = @_;
for(@$candidates){
push @{$self->chromosomes},
AI::Genetic::Pro::Chromosome->new_from_data($self->_translations, $self->type, $self->_package, $_, $self->_fix_range);
$self->_fitness->{$#{$self->chromosomes}} = $self->fitness()->($self, $self->chromosomes->[-1]);
}
$self->_strict( [ ] );
$self->population( $self->population + scalar( @$candidates ) );
return 1;
}
#=======================================================================
sub _state {
lib/AI/Genetic/Pro.pm view on Meta::CPAN
# ALIASES ##############################################################
#=======================================================================
sub people { $_[0]->chromosomes() }
#=======================================================================
sub getHistory { $_[0]->_history() }
#=======================================================================
sub mutProb { shift->mutation(@_) }
#=======================================================================
sub crossProb { shift->crossover(@_) }
#=======================================================================
sub intType { shift->type() }
#=======================================================================
# STATS ################################################################
#=======================================================================
sub getFittest_as_arrayref {
my ($self, $n, $uniq) = @_;
$n ||= 1;
$self->_calculate_fitness_all() unless scalar %{ $self->_fitness };
my @keys = sort { $self->_fitness->{$a} <=> $self->_fitness->{$b} } 0..$#{$self->chromosomes};
lib/AI/Genetic/Pro.pm view on Meta::CPAN
sub terminate {
my ($ga) = @_;
my $result = oct('0b' . $ga->as_string($ga->getFittest));
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
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=over 8
=item -fitness
This defines a I<fitness> function. It expects a reference to a subroutine.
=item -terminate
This defines a I<terminate> function. It expects a reference to a subroutine.
=item -type
This defines the type of chromosomes. Currently, C<AI::Genetic::Pro> supports four types:
=over 12
=item bitvector
Individuals/chromosomes of this type have genes that are bits. Each gene can be in one of two possible states, on or off.
=item listvector
Each gene of a "listvector" individual/chromosome can assume one string value from a specified list of possible string values.
=item rangevector
Each gene of a "rangevector" individual/chromosome can assume one integer
value from a range of possible integer values. Note that only integers
are supported. The user can always transform any desired fractional values
lib/AI/Genetic/Pro.pm view on Meta::CPAN
-preserve => 1, # only one chromosome will be preserved
# or
-preserve => 9, # 9 chromosomes will be preserved
# and so on...
Attention! You cannot preserve more chromosomes than exist in your population.
=item -variable_length
This defines whether variable-length chromosomes are turned on (default off)
and a which types of mutation are allowed. See below.
=over 8
=item level 0
Feature is inactive (default). Example:
-variable_length => 0
# chromosomes (i.e. bitvectors)
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=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
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=back
=back
=item -strategy
This defines the astrategy of crossover operation. It expects an array
reference listed below:
-strategy => [ $type, @params ]
where type is one of:
=over 4
=item PointsSimple
Simple crossover in one or many points. The best chromosomes/individuals are
selected for the new generation. For example:
-strategy => [ 'PointsSimple', $n ]
lib/AI/Genetic/Pro.pm view on Meta::CPAN
for my $idx (sort { $b <=> $a } @remove){
splice @{$ga->chromosomes}, $idx, 1;
}
=item I<$ga>-E<gt>B<population>($population)
Set/get size of the population. This defines the size of the population, i.e. how many chromosomes to simultaneously exist at each generation.
=item I<$ga>-E<gt>B<indType>()
Get type of individuals/chromosomes. Currently supported types are:
=over 4
=item C<bitvector>
Chromosomes will be just bitvectors. See documentation of C<new> method.
=item C<listvector>
Chromosomes will be lists of specified values. See documentation of C<new> method.
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=item C<combination>
Chromosomes will be unique lists of specified values. This is used for example
in the I<Traveling Salesman Problem>. See the documentation of the C<new>
method.
=back
In example:
my $type = $ga->type();
=item I<$ga>-E<gt>B<type>()
Alias for C<indType>.
=item I<$ga>-E<gt>B<crossProb>()
This method is used to query and set the crossover rate.
=item I<$ga>-E<gt>B<crossover>()
Alias for C<crossProb>.
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=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.
$ga->init(10);
This initializes a population where each individual/chromosome has 10 genes.
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=item I<$ga>-E<gt>B<as_array_def_only>($chromosome)
In list context return an array representing the specified chromosome.
In scalar context return an reference to an array representing the specified
chromosome. If I<variable_length> is turned off, this function is just an
alias for C<as_array>. If I<variable_length> is turned on and is set to
level 2, this function will return only C<not undef> values from chromosome.
See example below:
# -variable_length => 2, -type => 'bitvector'
my @chromosome = $ga->as_array($chromosome)
# @chromosome looks something like that
# ( undef, undef, undef, 1, 0, 1, 1, 1, 0 )
@chromosome = $ga->as_array_def_only($chromosome)
# @chromosome looks something like that
# ( 1, 0, 1, 1, 1, 0 )
=item I<$ga>-E<gt>B<as_string>($chromosome)
Return a string representation of the specified chromosome. See example below:
# -type => 'bitvector'
my $string = $ga->as_string($chromosome);
# $string looks something like that
# 1___0___1___1___1___0
# or
# -type => 'listvector'
$string = $ga->as_string($chromosome);
# $string looks something like that
# element0___element1___element2___element3...
Attention! If I<variable_length> is turned on and is set to level 2, it is
possible to get C<undef> values on the left side of the vector. In the returned
string C<undef> values will be replaced with B<spaces>. If you don't want
to see any I<spaces>, use C<as_string_def_only> instead of C<as_string>.
=item I<$ga>-E<gt>B<as_string_def_only>($chromosome)
Return a string representation of specified chromosome. If I<variable_length>
is turned off, this function is just alias for C<as_string>. If I<variable_length>
is turned on and is set to level 2, this function will return a string without
C<undef> values. See example below:
# -variable_length => 2, -type => 'bitvector'
my $string = $ga->as_string($chromosome);
# $string looks something like that
# ___ ___ ___1___1___0
$string = $ga->as_string_def_only($chromosome);
# $string looks something like that
# 1___1___0
=item I<$ga>-E<gt>B<as_value>($chromosome)
lib/AI/Genetic/Pro/Array/Type.pm view on Meta::CPAN
get_array_ref_by_element_size
);
#-----------------------------------------------------------------------
our $Native = 0;
#=======================================================================
sub get_package_by_element_size {
return if $Native;
my $size = shift;
my $type = #$size < 32 ? undef : # Pure Perl array
#$size < 32 ? 'AI::Genetic::Pro::Array::Tied' : # Pure Perl array
$size < 128 ? 'Tie::Array::Packed::Char' : # 8 bits
$size < 256 ? 'Tie::Array::Packed::UnsignedChar' : # 8 bits
$size < 65_537 ? 'Tie::Array::Packed::ShortNative' : # 16 bits
$size < 131_073 ? 'Tie::Array::Packed::UnsignedShortNative' : # 16 bits
$size < 2_147_483_648 ? 'Tie::Array::Packed::Integer' : # 32 bits
$size < 4_294_967_297 ? 'Tie::Array::Packed::UnsignedInteger' : # 32 bits; MAX
undef;
return unless $type;
return $type;
}
#=======================================================================
sub get_array_ref_by_element_size {
my $package = get_package_by_element_size(shift);
my @array;
tie @array, $package if $package;
return \@array;
}
#=======================================================================
1;
lib/AI/Genetic/Pro/Chromosome.pm view on Meta::CPAN
package AI::Genetic::Pro::Chromosome;
$AI::Genetic::Pro::Chromosome::VERSION = '1.009';
use warnings;
use strict;
use List::Util qw(shuffle first);
use List::MoreUtils qw(first_index);
use Tie::Array::Packed;
#use Math::Random qw(random_uniform_integer);
#=======================================================================
sub new {
my ($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;
my @genes;
tie @genes, $package if $package;
if($type eq q/bitvector/){
die qq/\nInproper value in the injected chromosome of type "$type": @$values\n/
if first { not defined $_ or ($_ != 0 and $_ != 1) } @$values;
@genes = @$values;
}elsif($type eq q/combination/){
die qq/\nToo few elements in the injected chromosome of type "$type": @$values\n/
if $#$values != $#{$data->[0]};
for my $idx(0..$#$values){
my $id = first_index { $_ eq $values->[$idx] } @{$data->[0]}; # pomijamy poczatkowy undef
die qq/\nInproper element in the injected chromosome of type "$type": @$values\n/ if $id == -1;
push @genes, $id;
}
}elsif($type eq q/rangevector/){
for my $idx(0..$#$values){
if(defined $values->[$idx]){
my $min = $data->[$idx]->[1] - $fix_range->[$idx];
my $max = $data->[$idx]->[2] - $fix_range->[$idx];
die qq/\nValue out of scope in the injected chromosome of type "$type": @$values\n/
if $values->[$idx] > $max or $values->[$idx] < $min;
push @genes, $values->[$idx] + $fix_range->[$idx];
}else{ push @genes, 0; }
}
}else{
for my $idx(0..$#$values){
my $id = first_index {
not defined $values->[$idx] and not defined $_ or
defined $_ and defined $values->[$idx] and $_ eq $values->[$idx]
} @{$data->[$idx]}; # pomijamy poczatkowy undef
die qq/\nInproper element in the injected chromosome of type "$type": @$values\n/ if $id == -1;
push @genes, $id;
}
}
return bless \@genes, $class;
}
#=======================================================================
sub clone
{
my ( $self ) = @_;
lib/AI/Genetic/Pro/Crossover/Distribution.pm view on Meta::CPAN
random_normal
random_beta
random_binomial
random_chi_square
random_exponential
random_poisson
);
use List::MoreUtils qw(first_index);
#=======================================================================
sub new {
my ($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]};
lib/AI/Genetic/Pro/Crossover/Distribution.pm view on Meta::CPAN
unless(scalar @elders){
$_fitness->{scalar(@children)} = $fitness->($ga, $chromosomes->[$elders[0]]);
push @children, $chromosomes->[$elders[0]];
next;
}
#~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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;
$max = $#{$chromosomes->[$el]} if $#{$chromosomes->[$el]} < $max;
}
lib/AI/Genetic/Pro/Selection/Distribution.pm view on Meta::CPAN
random_normal
random_beta
random_binomial
random_chi_square
random_exponential
random_poisson
);
use Carp 'croak';
#=======================================================================
sub new {
my ($class, $type, @params) = @_;
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/RouletteDistribution.pm view on Meta::CPAN
random_normal
random_beta
random_binomial
random_chi_square
random_exponential
random_poisson
);
use Carp 'croak';
#=======================================================================
sub new {
my ($class, $type, @params) = @_;
bless {
type => $type,
params => \@params,
}, $class;
}
#=======================================================================
sub roulette {
my ($total, $wheel) = @_;
my $rand = rand($total);
my $idx = first_index { $_->[1] > $rand } @$wheel;
if($idx == 0){ $idx = 1 }
elsif($idx == -1 ) { $idx = scalar @$wheel; }
lib/AI/Genetic/Pro/Selection/RouletteDistribution.pm view on Meta::CPAN
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
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
t/02_cache.t view on Meta::CPAN
}
sub fitness {
my ($ga, $chromosome) = @_;
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
t/02_cache.t view on Meta::CPAN
$ga->chromosomes( [ ] );
$ga->inject( [ [ qw( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1) ] ] );
my $start = [Time::HiRes::gettimeofday()];
$ga->as_value($ga->chromosomes->[0]) for 0..10000;
my $time0 =Time::HiRes::tv_interval($start);
$ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => sub { return; }, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 10, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
t/03_bitvectors_constant_length.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
t/04_bitvectors_variable_length_I.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
t/05_bitvectors_variable_length_II.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
t/06_listvectors_constant_length.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
t/07_listvectors_variable_length_I.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
t/08_listvectors_variable_length_II.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.01, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
t/09_rangevectors_constant_length.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'rangevector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
t/10_rangevectors_variable_length_I.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'rangevector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
t/11_rangevectors_variable_length_II.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'rangevector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
t/12_combinations_constant_length.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'combination', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'PMX' ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
t/13_preserve.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 4, # remember the bests
-variable_length => 0, # turn variable length OFF
t/14_getFittest.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 4, # remember the bests
-variable_length => 0, # turn variable length OFF
t/15_bitvectors_constant_length_MCE.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
t/16_bitvectors_constant_length_-_native_arrays.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
t/17_bitvectors_constant_length_MCE_-_native_arrays.t view on Meta::CPAN
sub terminate {
my ($ga) = @_;
return 1 if $Win == $ga->as_value($ga->getFittest);
return;
}
my $ga = AI::Genetic::Pro->new(
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
( run in 4.020 seconds using v1.01-cache-2.11-cpan-df04353d9ac )