AI-Genetic-Pro
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0.401 Fri, 19 Nov 2011 19:22:45 +0100
- Some bug fixes.
0.341 Mon, 23 Mar 2009 17:21:52 +0100
- Fixed bug in a documentation. Thanks to Randal L. Schwartz :-)
0.34 Tue, 17 Mar 2009 20:39:16 +0100
- Fixed bug in PMX strategy. Thanks to Maciej Misiak :-)
0.335 Sat, 07 Feb 2009 20:04:52 +0100
- Little changes in a Makefile.PL (especially for Sun Solaris)
0.334 Fri, 23 Jan 2009 00:03:26 +0100
- Module 'Digest::MD5' is loaded by default,
0.333 Fri, 22 Jan 2009 15:30:06 +0100
- Some improvments in 'getFittest' function,
- Added 'getFittest_as_arrayref' function,
0.332 Wed, 21 Jan 2009 00:31:01 +0100
- Some changes in tests.
0.331 Tue, 20 Jan 2009 23:55:20 +0100
- Added tests.
- Some improvments in the 'inject' function.
0.33 Mon, 19 Jan 2009 00:38:06 +0100
- Added 'strict' mode.
- Added 'inject' function.
0.32 Sun, 18 Jan 2009 01:16:37 +0100
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# load state of GA
$ga->load('genetic.sga');
DESCRIPTION
This module provides efficient implementation of a genetic algorithm
for professional purpose with support for multiprocessing. It was
designed to operate as fast as possible even on very large populations
and big individuals/chromosomes. AI::Genetic::Pro was inspired by
AI::Genetic, so it is in most cases compatible (there are some
changes). Additionally AI::Genetic::Pro isn't a pure Perl solution, so
it doesn't have limitations of its ancestor (such as slow-down in the
case of big populations ( >10000 ) or vectors with more than 33
fields).
If You are looking for a pure Perl solution, consider AI::Genetic.
Speed
To increase speed XS code is used, however with portability in mind.
This distribution was tested on Windows and Linux platforms (and
$ga->generation()
Get the number of the current generation.
$ga->people()
Returns an anonymous list of individuals/chromosomes of the current
population.
IMPORTANT: the actual array reference used by the AI::Genetic::Pro
object is returned, so any changes to it will be reflected in $ga.
$ga->chromosomes()
Alias for people.
$ga->chart(%options)
Generate a chart describing changes of min, mean, and max scores in
your population. To satisfy your needs, you can pass the following
options:
-filename
File to save a chart in (obligatory).
-title
Title of a chart (default: Evolution).
Class::Accessor::Fast::XS = 0
Storable = 2.05
GD::Graph::linespoints = 1.54
MCE = 1.874
MCE::Map = 1.874
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lib/AI/Genetic/Pro.pm view on Meta::CPAN
# load state of GA
$ga->load('genetic.sga');
=head1 DESCRIPTION
This module provides efficient implementation of a genetic algorithm for
professional purpose with support for multiprocessing. It was designed to operate as fast as possible
even on very large populations and big individuals/chromosomes. C<AI::Genetic::Pro>
was inspired by C<AI::Genetic>, so it is in most cases compatible
(there are some changes). Additionally C<AI::Genetic::Pro> isn't a pure Perl solution, so it
doesn't have limitations of its ancestor (such as slow-down in the
case of big populations ( >10000 ) or vectors with more than 33 fields).
If You are looking for a pure Perl solution, consider L<AI::Genetic>.
=over 4
=item Speed
To increase speed XS code is used, however with portability in
lib/AI/Genetic/Pro.pm view on Meta::CPAN
=item I<$ga>-E<gt>B<generation>()
Get the number of the current generation.
=item I<$ga>-E<gt>B<people>()
Returns an anonymous list of individuals/chromosomes of the current population.
B<IMPORTANT:> the actual array reference used by the C<AI::Genetic::Pro>
object is returned, so any changes to it will be reflected in I<$ga>.
=item I<$ga>-E<gt>B<chromosomes>()
Alias for C<people>.
=item I<$ga>-E<gt>B<chart>(%options)
Generate a chart describing changes of min, mean, and max scores in your
population. To satisfy your needs, you can pass the following options:
=over 4
=item -filename
File to save a chart in (B<obligatory>).
=item -title
lib/AI/Genetic/Pro/Mutation/Bitvector.pm view on Meta::CPAN
#@{ $chromosomes->[ $idx ] } = reverse @{ $chromosomes->[ $idx ] };
}else{
my $id = int rand @{$chromosomes->[$idx]};
$chromosomes->[$idx]->[$id] = $chromosomes->[$idx]->[$id] ? 0 : 1;
}
}else{
my $id = int rand @{$chromosomes->[$idx]};
$chromosomes->[$idx]->[$id] = $chromosomes->[$idx]->[$id] ? 0 : 1;
}
# we need to change fitness
$_fitness->{$idx} = $fitness->($ga, $chromosomes->[$idx]);
}
return 1;
}
#=======================================================================
# too slow; mutation is too dangerous in this solution
sub run0 {
my ($self, $ga) = @_;
lib/AI/Genetic/Pro/Mutation/Combination.pm view on Meta::CPAN
elsif($rand < $mutation){
my $el = int rand @{$chromosomes->[$idx]};
my $new = int rand @{$_translations->[0]};
next if $new == $chromosomes->[$idx]->[$el];
my $id = first_index { $_ == $new } @{$chromosomes->[$idx]};
$chromosomes->[$idx]->[$id] = $chromosomes->[$idx]->[$el] if defined $id and $id != -1;
$chromosomes->[$idx]->[$el] = $new;
}
# we need to change fitness
$_fitness->{$idx} = $fitness->($ga, $chromosomes->[$idx]);
}
return 1;
}
#=======================================================================
1;
lib/AI/Genetic/Pro/Mutation/Listvector.pm view on Meta::CPAN
}
}else{
my $id = $min + int rand($range - 1);
$chromosomes->[$idx]->[$id] = 1 + int rand $#{$_translations->[$id]};
}
}else{
my $id = int rand @{$chromosomes->[$idx]};
$chromosomes->[$idx]->[$id] = 1 + int rand $#{$_translations->[$id]};
}
# we need to change fitness
$_fitness->{$idx} = $fitness->($ga, $chromosomes->[$idx]);
}
return 1;
}
#=======================================================================
1;
lib/AI/Genetic/Pro/Mutation/Rangevector.pm view on Meta::CPAN
}
}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]};
$chromosomes->[$idx]->[$id] = random_uniform_integer(1, @{$_translations->[$id]}[1..2]);
}
# we need to change fitness
$_fitness->{$idx} = $fitness->($ga, $chromosomes->[$idx]);
}
return 1;
}
#=======================================================================
sub run0 {
my ($self, $ga) = @_;
# this is declared here just for speed
( run in 0.876 second using v1.01-cache-2.11-cpan-c333fce770f )