AI-Genetic-Pro
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-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 1000, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.01, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 1, # remember best results
-preserve => 3, # remember the bests
-variable_length => 1, # turn variable length ON
-mce => 1, # optional MCE support
-workers => 3, # number of workers (MCE)
);
# init population of 32-bit vectors
$ga->init(32);
# evolve 10 generations
$ga->evolve(10);
# best score
print "SCORE: ", $ga->as_value($ga->getFittest), ".\n";
# save evolution path as a chart
$ga->chart(-filename => 'evolution.png');
# save state of GA
$ga->save('genetic.sga');
# load state of GA
$ga->load('genetic.sga');
This defines the crossover rate. The fairest results are achieved
with crossover rate ~0.95.
-mutation
This defines the mutation rate. The fairest results are achieved
with mutation rate ~0.01.
-preserve
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
where type is one of:
RouletteBasic
Each individual/chromosome can be selected with probability
proportional to its fitness.
Roulette
First the best individuals/chromosomes are selected. From this
collection parents are selected with probability poportional to
their fitness.
RouletteDistribution
Each individual/chromosome has a portion of roulette wheel
proportional to its fitness. Selection is done with the specified
distribution. Supported distributions and parameters are listed
below.
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.
PointsBasic
Crossover in one or many points. In basic crossover selected
parents are crossed and one (randomly-chosen) child is moved to
the new generation. For example:
-strategy => [ 'PointsBasic', $n ]
where $n is the number of points for crossing.
Points
Crossover in one or many points. In normal crossover selected
parents are crossed and the best child is moved to the new
generation. For example:
-strategy => [ 'Points', $n ]
where $n is number of points for crossing.
PointsAdvenced
Crossover in one or many points. After crossover the best
chromosomes/individuals from all parents and chidren are selected
for the new generation. For example:
-strategy => [ 'PointsAdvanced', $n ]
where $n is the number of points for crossing.
Distribution
In distribution crossover parents are crossed in points selected
my ($max, $mean, $min) = $ga->getAvgFitness();
$ga->getFittest($n, $unique)
This function returns a list of the fittest chromosomes from the
current population. You can specify how many chromosomes should be
returned and if the returned chromosomes should be unique. See
example below.
# only one - the best
my ($best) = $ga->getFittest;
# or 5 bests chromosomes, NOT unique
my @bests = $ga->getFittest(5);
# or 7 bests and UNIQUE chromosomes
my @bests = $ga->getFittest(7, 1);
If you want to get a large number of chromosomes, try to use the
getFittest_as_arrayref function instead (for efficiency).
$ga->getFittest_as_arrayref($n, $unique)
This function is very similar to getFittest, but it returns a
reference to an array instead of a list.
$ga->generation()
lib/AI/Genetic/Pro.pm view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 1000, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.01, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 1, # remember best results
-preserve => 3, # remember the bests
-variable_length => 1, # turn variable length ON
-mce => 1, # optional MCE support
-workers => 3, # number of workers (MCE)
);
# init population of 32-bit vectors
$ga->init(32);
# evolve 10 generations
$ga->evolve(10);
# best score
print "SCORE: ", $ga->as_value($ga->getFittest), ".\n";
# save evolution path as a chart
$ga->chart(-filename => 'evolution.png');
# save state of GA
$ga->save('genetic.sga');
# load state of GA
$ga->load('genetic.sga');
lib/AI/Genetic/Pro.pm view on Meta::CPAN
This defines the crossover rate. The fairest results are achieved with
crossover rate ~0.95.
=item -mutation
This defines the mutation rate. The fairest results are achieved with mutation
rate ~0.01.
=item -preserve
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
lib/AI/Genetic/Pro.pm view on Meta::CPAN
where type is one of:
=over 8
=item B<RouletteBasic>
Each individual/chromosome can be selected with probability proportional to its fitness.
=item B<Roulette>
First the best individuals/chromosomes are selected. From this collection
parents are selected with probability poportional to their fitness.
=item B<RouletteDistribution>
Each individual/chromosome has a portion of roulette wheel proportional to its
fitness. Selection is done with the specified distribution. Supported
distributions and parameters are listed below.
=over 12
lib/AI/Genetic/Pro.pm view on Meta::CPAN
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', $n ]
where C<$n> is the number of points for crossing.
=item PointsBasic
Crossover in one or many points. In basic crossover selected parents are
crossed and one (randomly-chosen) child is moved to the new generation. For
example:
-strategy => [ 'PointsBasic', $n ]
where C<$n> is the number of points for crossing.
=item Points
Crossover in one or many points. In normal crossover selected parents are crossed and the best child is moved to the new generation. For example:
-strategy => [ 'Points', $n ]
where C<$n> is number of points for crossing.
=item PointsAdvenced
Crossover in one or many points. After crossover the best
chromosomes/individuals from all parents and chidren are selected for the new
generation. For example:
-strategy => [ 'PointsAdvanced', $n ]
where C<$n> is the number of points for crossing.
=item Distribution
In I<distribution> crossover parents are crossed in points selected with the
lib/AI/Genetic/Pro.pm view on Meta::CPAN
Get I<max>, I<mean> and I<min> score of the current generation. In example:
my ($max, $mean, $min) = $ga->getAvgFitness();
=item I<$ga>-E<gt>B<getFittest>($n, $unique)
This function returns a list of the fittest chromosomes from the current
population. You can specify how many chromosomes should be returned and if
the returned chromosomes should be unique. See example below.
# only one - the best
my ($best) = $ga->getFittest;
# or 5 bests chromosomes, NOT unique
my @bests = $ga->getFittest(5);
# or 7 bests and UNIQUE chromosomes
my @bests = $ga->getFittest(7, 1);
If you want to get a large number of chromosomes, try to use the
C<getFittest_as_arrayref> function instead (for efficiency).
=item I<$ga>-E<gt>B<getFittest_as_arrayref>($n, $unique)
This function is very similar to C<getFittest>, but it returns a reference
to an array instead of a list.
=item I<$ga>-E<gt>B<generation>()
t/01_inject.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
my $population = [ ];
for my $chromosome(@{$ga->chromosomes}){
push @$population, $chromosome->clone;
}
t/02_cache.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => sub { return; }, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 10, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 0, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
$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
-fitness => \&fitness, # fitness function
-terminate => sub { return; }, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 10, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
$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
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
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/04_bitvectors_variable_length_I.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
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/05_bitvectors_variable_length_II.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
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/06_listvectors_constant_length.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
t/07_listvectors_variable_length_I.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
);
my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
t/08_listvectors_variable_length_II.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'listvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.01, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
);
my @data;
push @data, [ MIN..MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
t/09_rangevectors_constant_length.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'rangevector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
t/10_rangevectors_variable_length_I.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'rangevector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 1, # turn variable length OFF
);
my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
t/11_rangevectors_variable_length_II.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'rangevector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 2, # turn variable length OFF
);
my @data;
push @data, [ MIN, MAX ] for 1..SIZE;
$ga->init(\@data);
@data = (
[qw( 4 0 4 0 4 0 4 0 )],
t/12_combinations_constant_length.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'combination', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'PMX' ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn variable length OFF
);
$ga->init( [ 'a'..'h' ] );
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 )],
t/13_preserve.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 4, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
my @Win0 = @Win; $Win0[-1] = 0;
my @Win1 = @Win; $Win1[-2] = 0;
my @Win2 = @Win; $Win2[-1] = 0; $Win2[-2] = 0;
t/14_getFittest.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 4, # remember the bests
-variable_length => 0, # turn variable length OFF
);
# init population of 32-bit vectors
$ga->init(BITS);
$ga->inject( [ \@Win, \@Win, \@Win, \@Win ] );
# evolve 1000 generations
$ga->evolve(1);
t/15_bitvectors_constant_length_MCE.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
-mce => 1, # turn ON Many-Core Engine
);
# init population of 32-bit vectors
$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 ) ],
t/16_bitvectors_constant_length_-_native_arrays.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
-native => 1, # turn ON use of native arrays
);
# init population of 32-bit vectors
$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 ) ],
t/17_bitvectors_constant_length_MCE_-_native_arrays.t view on Meta::CPAN
-fitness => \&fitness, # fitness function
-terminate => \&terminate, # terminate function
-type => 'bitvector', # type of chromosomes
-population => 100, # population
-crossover => 0.9, # probab. of crossover
-mutation => 0.05, # probab. of mutation
-parents => 2, # number of parents
-selection => [ 'Roulette' ], # selection strategy
-strategy => [ 'Points', 2 ], # crossover strategy
-cache => 1, # cache results
-history => 0, # remember best results
-preserve => 0, # remember the bests
-variable_length => 0, # turn OFF variable length
-mce => 1, # turn ON Many-Core Engine
-native => 1, # turn ON use of native arrays
);
# 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 ) ],
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