AI-Genetic-Pro
view release on metacpan or search on metacpan
0.32 Sun, 18 Jan 2009 01:16:37 +0100
- Fixes in rangevectors.
0.31 Sat, 17 Jan 2009 20:32:27 +0100
- Fixes in mutation. Added changing of fitness for mutated chromosomes.
0.30 Fri, 16 Jan 2009 00:06:29 +0100
- Fixes in as_string method.
0.29 Thu, 15 Jan 2009 21:13:15 +0100
- Added full support for variable-length chromosomes.
0.28
- Little fixes. Again :-)
0.27
- Little fixes.
0.26 Sun, 11 Jan 2009 22:04:26 +0100
- Little modifications in crossover.
- Added supprot for variable-length chromosomes. Thanks to Leonid Zamdborg :-)
0.25 Mon, 24 Nov 2008 18:57:27 +0100
- Little fix in a counter of generations.
0.24 Mon, 10 Nov 2008 11:34:12 +0100
- Little speed up in main loop.
0.23 Mon, 10 Nov 2008 10:14:32 +0100
- Little modification in main loop. Infinity loop available.
When a "work that uses the Library" uses material from a
header file that is part of the Library, the object code for the
work may be a derivative work of the Library even though the
source code is not. Whether this is true is especially
significant if the work can be linked without the Library, or if
the work is itself a library. The threshold for this to be true is
not precisely defined by law.
If such an object file uses only numerical parameters, data
structure layouts and accessors, and small macros and
small inline functions (ten lines or less in length), then the
use of the object file is unrestricted, regardless of whether it
is legally a derivative work. (Executables containing this
object code plus portions of the Library will still fall under
Section 6.)
Otherwise, if the work is a derivative of the Library, you may
distribute the object code for the work under the terms of
Section 6. Any executables containing that work also fall
under Section 6, whether or not they are linked directly with
the Library itself.
lib/AI/Genetic/Pro/Mutation/Combination.pm
lib/AI/Genetic/Pro/Mutation/Listvector.pm
lib/AI/Genetic/Pro/Mutation/Rangevector.pm
lib/AI/Genetic/Pro/Selection/Distribution.pm
lib/AI/Genetic/Pro/Selection/Roulette.pm
lib/AI/Genetic/Pro/Selection/RouletteBasic.pm
lib/AI/Genetic/Pro/Selection/RouletteDistribution.pm
t/00_load.t
t/01_inject.t
t/02_cache.t
t/03_bitvectors_constant_length.t
t/04_bitvectors_variable_length_I.t
t/05_bitvectors_variable_length_II.t
t/06_listvectors_constant_length.t
t/07_listvectors_variable_length_I.t
t/08_listvectors_variable_length_II.t
t/09_rangevectors_constant_length.t
t/10_rangevectors_variable_length_I.t
t/11_rangevectors_variable_length_II.t
t/12_combinations_constant_length.t
t/13_preserve.t
t/14_getFittest.t
t/15_bitvectors_constant_length_MCE.t
t/16_bitvectors_constant_length_-_native_arrays.t
t/17_bitvectors_constant_length_MCE_-_native_arrays.t
-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)
);
# init population of 32-bit vectors
$ga->init(32);
# evolve 10 generations
$ga->evolve(10);
will be preserved, i.e.
-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.
-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
0 1 1 1 0 1 0 0 1 1 0 1 1 1
0 1 0 0 1 1 0 1 1 1 1 0 1 0
# ...and so on
level 1
Feature is active, but chromosomes can varies only on the right
side, Example:
-variable_length => 1
# chromosomes (i.e. bitvectors)
0 1 0 0 1 1 0 1 1 1
0 0 1 1 0 1 1 1 1
0 1 1 1 0 1 0 0 1 1 0 1 1 1
0 1 0 0 1 1 0 1 1 1
# ...and so on
level 2
Feature is active and chromosomes can varies on the left side and
on the right side; unwanted values/genes on the left side are
replaced with undef, ie.
-variable_length => 2
# chromosomes (i.e. bitvectors)
x x x 0 1 1 0 1 1 1
x x x x 0 1 1 1 1
x 1 1 1 0 1 0 0 1 1 0 1 1 1
0 1 0 0 1 1 0 1 1 1
# where 'x' means 'undef'
# ...and so on
In this situation returned chromosomes in an array context
$ga->init($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.
listvector
For listvectors, the argument is an anonymous list of lists. The
number of sub-lists is equal to the number of genes of each
file.
$ga->load($file)
Load a state of the genetic algorithm from the specified file.
$ga->as_array($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
on and is set to level 2, an array can have some undef values. To get
only not undef values use as_array_def_only instead of as_array.
$ga->as_array_def_only($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)
# 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)
Alun Jones for fixing memory leaks.
Tod Hagan for reporting a bug (rangevector values truncated to signed
8-bit quantities) and supplying a patch.
Randal L. Schwartz for reporting a bug in this documentation.
Maciej Misiak for reporting problems with combination (and a bug in a
PMX strategy).
LEONID ZAMDBORG for recommending the addition of variable-length
chromosomes as well as supplying relevant code samples, for testing and
at the end reporting some bugs.
Christoph Meissner for reporting a bug.
Alec Chen for reporting some bugs.
AUTHOR
Strzelecki Lukasz <lukasz@strzeleccy.eu>
lib/AI/Genetic/Pro.pm view on Meta::CPAN
parents _parents
history _history
fitness _fitness _fitness_real
cache
mutation _mutator
strategy _strategist
selection _selector
_translations
generation
preserve
variable_length
_fix_range
_package
_length
strict _strict
workers
size
_init
));
#=======================================================================
# Additional modules
use constant STORABLE => 'Storable';
use constant GD => 'GD::Graph::linespoints';
#=======================================================================
lib/AI/Genetic/Pro.pm view on Meta::CPAN
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);
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;
#-------------------------------------------------------------------
delete $self->{ mce };
'AI::Genetic::Pro::MCE'->use or die q[Cannot raise multicore support: ] . $@;
lib/AI/Genetic/Pro.pm view on Meta::CPAN
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;
$self->_calculate_fitness_all();
}
#=======================================================================
# SAVE / LOAD ##########################################################
#=======================================================================
sub spew {
#+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
STORABLE->use( qw( store retrieve freeze thaw ) ) or croak(q/You need "/.STORABLE.q/" module to save a state of "/.__PACKAGE__.q/"!/);
lib/AI/Genetic/Pro.pm view on Meta::CPAN
return 1;
}
#=======================================================================
# TRANSLATIONS #########################################################
#=======================================================================
sub as_array_def_only {
my ($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;
}
lib/AI/Genetic/Pro.pm view on Meta::CPAN
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]));
lib/AI/Genetic/Pro.pm view on Meta::CPAN
-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)
);
# init population of 32-bit vectors
$ga->init(32);
# evolve 10 generations
$ga->evolve(10);
lib/AI/Genetic/Pro.pm view on Meta::CPAN
This defines injection of the bests chromosomes into a next generation. It causes a little slow down, however (very often) much better results are achieved. You can specify, how many chromosomes will be preserved, i.e.
-preserve => 1, # only one chromosome will be preserved
# or
-preserve => 9, # 9 chromosomes will be preserved
# and so on...
Attention! You cannot preserve more chromosomes than exist in your population.
=item -variable_length
This defines whether variable-length chromosomes are turned on (default off)
and a which types of mutation are allowed. See below.
=over 8
=item level 0
Feature is inactive (default). Example:
-variable_length => 0
# chromosomes (i.e. bitvectors)
0 1 0 0 1 1 0 1 1 1 0 1 0 1
0 0 1 1 0 1 1 1 1 0 0 1 1 0
0 1 1 1 0 1 0 0 1 1 0 1 1 1
0 1 0 0 1 1 0 1 1 1 1 0 1 0
# ...and so on
=item level 1
Feature is active, but chromosomes can varies B<only on the right side>, Example:
-variable_length => 1
# chromosomes (i.e. bitvectors)
0 1 0 0 1 1 0 1 1 1
0 0 1 1 0 1 1 1 1
0 1 1 1 0 1 0 0 1 1 0 1 1 1
0 1 0 0 1 1 0 1 1 1
# ...and so on
=item level 2
Feature is active and chromosomes can varies B<on the left side and on
the right side>; unwanted values/genes on the left side are replaced with C<undef>, ie.
-variable_length => 2
# chromosomes (i.e. bitvectors)
x x x 0 1 1 0 1 1 1
x x x x 0 1 1 1 1
x 1 1 1 0 1 0 0 1 1 0 1 1 1
0 1 0 0 1 1 0 1 1 1
# where 'x' means 'undef'
# ...and so on
In this situation returned chromosomes in an array context ($ga-E<gt>as_array($chromosome))
lib/AI/Genetic/Pro.pm view on Meta::CPAN
Set/get number of parents in a crossover.
=item I<$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.
=item B<listvector>
For listvectors, the argument is an anonymous list of lists. The number of sub-lists is equal to the number of genes of each individual/chromosome. Each sub-list defines the possible string values that the corresponding gene can assume.
$ga->init([
lib/AI/Genetic/Pro.pm view on Meta::CPAN
Save the current state of the genetic algorithm to the specified file.
=item I<$ga>-E<gt>B<load>($file)
Load a state of the genetic algorithm from the specified file.
=item I<$ga>-E<gt>B<as_array>($chromosome)
In list context return an array representing the specified chromosome.
In scalar context return an reference to an array representing the specified
chromosome. If I<variable_length> is turned on and is set to level 2, an array
can have some C<undef> values. To get only C<not undef> values use
C<as_array_def_only> instead of C<as_array>.
=item I<$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)
lib/AI/Genetic/Pro.pm view on Meta::CPAN
# 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.pm view on Meta::CPAN
Miles Gould for suggestions and some fixes (even in this documentation! :-).
Alun Jones for fixing memory leaks.
Tod Hagan for reporting a bug (rangevector values truncated to signed 8-bit quantities) and supplying a patch.
Randal L. Schwartz for reporting a bug in this documentation.
Maciej Misiak for reporting problems with C<combination> (and a bug in a PMX strategy).
LEONID ZAMDBORG for recommending the addition of variable-length chromosomes as well as supplying relevant code samples, for testing and at the end reporting some bugs.
Christoph Meissner for reporting a bug.
Alec Chen for reporting some bugs.
=head1 AUTHOR
Strzelecki Lukasz <lukasz@strzeleccy.eu>
=head1 SEE ALSO
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;
lib/AI/Genetic/Pro/Crossover/Distribution.pm view on Meta::CPAN
@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;
}
}
$elders[0] = $chromosome->[$elders[0]]->clone;
for(0..$#seq){
next if not $seq[$_] or $_ < $min or $_ > $max;
lib/AI/Genetic/Pro/Crossover/Points.pm view on Meta::CPAN
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){
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;
lib/AI/Genetic/Pro/Crossover/PointsAdvanced.pm view on Meta::CPAN
#-------------------------------------------------------------------
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){
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;
lib/AI/Genetic/Pro/Crossover/PointsBasic.pm view on Meta::CPAN
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){
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;
lib/AI/Genetic/Pro/Crossover/PointsSimple.pm view on Meta::CPAN
#-------------------------------------------------------------------
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){
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;
lib/AI/Genetic/Pro/Mutation/Bitvector.pm view on Meta::CPAN
# this is declared here just for speed
my $mutation = $ga->mutation;
my $chromosomes = $ga->chromosomes;
my $_translations = $ga->_translations;
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
# main loop
for my $idx (0..$#$chromosomes){
next if rand() >= $mutation;
if($ga->variable_length){
my $rand = rand();
if($rand < 0.16 and $#{$chromosomes->[$idx]} > 1){
pop @{$chromosomes->[$idx]};
}elsif($rand < 0.32 and $#{$chromosomes->[$idx]} > 1){
shift @{$chromosomes->[$idx]};
}elsif($rand < 0.48 and $#{$chromosomes->[$idx]} < $#$_translations){
push @{$chromosomes->[$idx]}, rand > 0.5 ? 0 : 1;
}elsif($rand < 0.64 and $#{$chromosomes->[$idx]} < $#$_translations){
unshift @{$chromosomes->[$idx]}, rand > 0.5 ? 0 : 1;
}elsif($rand < 0.8){
lib/AI/Genetic/Pro/Mutation/Listvector.pm view on Meta::CPAN
# this is declared here just for speed
my $mutation = $ga->mutation;
my $chromosomes = $ga->chromosomes;
my $_translations = $ga->_translations;
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
# main loop
for my $idx (0..$#$chromosomes){
next if rand() >= $mutation;
if($ga->variable_length){
my $rand = rand();
my $min = first_index { $_ } @{$chromosomes->[$idx]};
my $range = $#{$chromosomes->[$idx]} - $min + 1;
if($rand < 0.4 and $range > 2){
if($rand < 0.2 and $ga->variable_length > 1){ $chromosomes->[$idx]->[$min] = 0; }
else{ pop @{$chromosomes->[$idx]}; }
}elsif($rand < 0.8 and $range < $#$_translations){
if($rand < 0.6 and $ga->variable_length > 1 and not $chromosomes->[$idx]->[0]){
$chromosomes->[$idx]->[ $min - 1 ] = 1 + int rand $#{$_translations->[ $min - 1 ]};
}elsif(exists $_translations->[scalar @{$chromosomes->[$idx]}]){
push @{$chromosomes->[$idx]}, 1 + int rand $#{$_translations->[scalar @{$chromosomes->[$idx]}]};
}
}else{
my $id = $min + int rand($range - 1);
$chromosomes->[$idx]->[$id] = 1 + int rand $#{$_translations->[$id]};
}
}else{
my $id = int rand @{$chromosomes->[$idx]};
lib/AI/Genetic/Pro/Mutation/Rangevector.pm view on Meta::CPAN
# this is declared here just for speed
my $mutation = $ga->mutation;
my $chromosomes = $ga->chromosomes;
my $_translations = $ga->_translations;
my ($fitness, $_fitness) = ($ga->fitness, $ga->_fitness);
# main loop
for my $idx (0..$#$chromosomes){
next if rand() >= $mutation;
if($ga->variable_length){
my $rand = rand();
my $min = first_index { $_ } @{$chromosomes->[$idx]};
my $range = $#{$chromosomes->[$idx]} - $min + 1;
if($rand < 0.4 and $range > 2){
if($rand < 0.2 and $ga->variable_length > 1){ $chromosomes->[$idx]->[$min] = 0; }
else{ pop @{$chromosomes->[$idx]}; }
}elsif($rand < 0.8 and $range < scalar @{$_translations}){
if($rand < 0.6 and $ga->variable_length > 1 and not $chromosomes->[$idx]->[0]){
$chromosomes->[$idx]->[ $min - 1 ] = random_uniform_integer(1, @{$_translations->[ $min - 1 ]}[1..2]);
}elsif(exists $_translations->[scalar @{$chromosomes->[$idx]}]){
push @{$chromosomes->[$idx]}, random_uniform_integer(1, @{$_translations->[scalar @{$chromosomes->[$idx]}]}[1..2]);
}
}else{
my $id = $min + int rand($range - 1);
$chromosomes->[$idx]->[$id] = random_uniform_integer(1, @{$_translations->[$id]}[1..2]);
}
}else{
my $id = int rand @{$chromosomes->[$idx]};
lib/AI/Genetic/Pro/Mutation/Rangevector.pm view on Meta::CPAN
sub run0 {
my ($self, $ga) = @_;
# this is declared here just for speed
my $mutation = $ga->mutation;
# main loop
foreach my $chromosome (@{$ga->{chromosomes}}){
next if rand() <= $mutation;
if($ga->variable_length){
my $rand = rand();
if($rand < 0.33 and $#$chromosome > 1){
pop @$chromosome;
}elsif($rand < 0.66 and $#$chromosome < $#{$ga->_translations}){
push @$chromosome, random_uniform_integer(1, @{$ga->_translations->[scalar @$chromosome]});
}else{
my $idx = int rand @$chromosome;
$chromosome->[$idx] = random_uniform_integer(1, @{$ga->_translations->[$idx]});
}
}else{
t/01_inject.t view on Meta::CPAN
-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);
my $population = [ ];
for my $chromosome(@{$ga->chromosomes}){
push @$population, $chromosome->clone;
}
t/02_cache.t view on Meta::CPAN
-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);
$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);
t/02_cache.t view on Meta::CPAN
-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);
$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) ] ] );
$start = [Time::HiRes::gettimeofday()];
$ga->as_value($ga->chromosomes->[0]) for 0..10000;
my $time1 =Time::HiRes::tv_interval($start);
t/03_bitvectors_constant_length.t view on Meta::CPAN
-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);
my @helper = (
[ qw( 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 ) ],
[ 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 0 0 0 0 0 0 0 0 ) ],
t/04_bitvectors_variable_length_I.t view on Meta::CPAN
-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);
my @helper = (
[ qw( 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 ) ],
[ 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 0 0 0 0 0 0 0 0 ) ],
t/05_bitvectors_variable_length_II.t view on Meta::CPAN
-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);
my @helper = (
[ qw( 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 ) ],
[ 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 0 0 0 0 0 0 0 0 ) ],
t/06_listvectors_constant_length.t view on Meta::CPAN
-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;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
t/07_listvectors_variable_length_I.t view on Meta::CPAN
-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;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
t/08_listvectors_variable_length_II.t view on Meta::CPAN
-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;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
t/09_rangevectors_constant_length.t view on Meta::CPAN
-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;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
t/10_rangevectors_variable_length_I.t view on Meta::CPAN
-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;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
t/11_rangevectors_variable_length_II.t view on Meta::CPAN
-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;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
[qw( 0 4 0 4 0 4 0 4 )],
t/12_combinations_constant_length.t view on Meta::CPAN
-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' ] );
my @data = (
[qw( a c b d e g f h )],
[qw( a b d c e f h g )],
[qw( a c b d f e g h )],
[qw( h b c d e f g a )],
t/13_preserve.t view on Meta::CPAN
-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);
my @Win0 = @Win; $Win0[-1] = 0;
my @Win1 = @Win; $Win1[-2] = 0;
my @Win2 = @Win; $Win2[-1] = 0; $Win2[-2] = 0;
$ga->inject( [ \@Win, \@Win0, \@Win1, \@Win2 ] );
t/14_getFittest.t view on Meta::CPAN
-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);
$ga->inject( [ \@Win, \@Win, \@Win, \@Win ] );
# evolve 1000 generations
$ga->evolve(1);
t/15_bitvectors_constant_length_MCE.t view on Meta::CPAN
-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
$ga->init(BITS);
my @helper = (
[ qw( 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 ) ],
t/16_bitvectors_constant_length_-_native_arrays.t view on Meta::CPAN
-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
$ga->init(BITS);
my @helper = (
[ qw( 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ) ],
[ qw( 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 ) ],
( run in 1.723 second using v1.01-cache-2.11-cpan-65fba6d93b7 )