AI-Genetic-Pro
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lib/AI/Genetic/Pro.pm view on Meta::CPAN
=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
by multiplying and dividing by an appropriate power of 10.
=item combination
Each gene of a "combination" individual/chromosome can assume one string value from a specified list of possible string values. B<All genes are unique.>
=back
=item -population
This defines the size of the population, i.e. how many chromosomes
simultaneously exist at each generation.
=item -crossover
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
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))
can have B<undef> values on the left side (only). In a scalar context each
undefined value is replaced with a single space. If You don't want to see
any C<undef> or space, just use C<as_array_def_only> and C<as_string_def_only>
instead of C<as_array> and C<as_string>.
=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
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
=item C<-selection =E<gt> [ 'RouletteDistribution', 'uniform' ]>
Standard uniform distribution. No additional parameters are needed.
=item C<-selection =E<gt> [ 'RouletteDistribution', 'normal', $av, $sd ]>
Normal distribution, where C<$av> is average (default: size of population /2) and $C<$sd> is standard deviation (default: size of population).
=item C<-selection =E<gt> [ 'RouletteDistribution', 'beta', $aa, $bb ]>
I<Beta> distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
C<$aa> and C<$bb> are set by default to number of parents.
B<Argument restrictions:> Both $aa and $bb must not be less than 1.0E-37.
=item C<-selection =E<gt> [ 'RouletteDistribution', 'binomial' ]>
Binomial distribution. No additional parameters are needed.
=item C<-selection =E<gt> [ 'RouletteDistribution', 'chi_square', $df ]>
Chi-square distribution with C<$df> degrees of freedom. C<$df> by default is set to size of population.
=item C<-selection =E<gt> [ 'RouletteDistribution', 'exponential', $av ]>
Exponential distribution, where C<$av> is average . C<$av> by default is set to size of population.
=item C<-selection =E<gt> [ 'RouletteDistribution', 'poisson', $mu ]>
Poisson distribution, where C<$mu> is mean. C<$mu> by default is set to size of population.
=back
=item B<Distribution>
Chromosomes/individuals are selected with specified distribution. See below.
=over 12
=item C<-selection =E<gt> [ 'Distribution', 'uniform' ]>
Standard uniform distribution. No additional parameters are needed.
=item C<-selection =E<gt> [ 'Distribution', 'normal', $av, $sd ]>
Normal distribution, where C<$av> is average (default: size of population /2) and $C<$sd> is standard deviation (default: size of population).
=item C<-selection =E<gt> [ 'Distribution', 'beta', $aa, $bb ]>
I<Beta> distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
C<$aa> and C<$bb> are set by default to number of parents.
B<Argument restrictions:> Both $aa and $bb must not be less than 1.0E-37.
=item C<-selection =E<gt> [ 'Distribution', 'binomial' ]>
Binomial distribution. No additional parameters are needed.
=item C<-selection =E<gt> [ 'Distribution', 'chi_square', $df ]>
Chi-square distribution with C<$df> degrees of freedom. C<$df> by default is set to size of population.
=item C<-selection =E<gt> [ 'Distribution', 'exponential', $av ]>
Exponential distribution, where C<$av> is average . C<$av> by default is set to size of population.
=item C<-selection =E<gt> [ 'Distribution', 'poisson', $mu ]>
Poisson distribution, where C<$mu> is mean. C<$mu> by default is set to size of population.
=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', $n ]
where C<$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
specified distribution. See below.
=over 8
=item C<-strategy =E<gt> [ 'Distribution', 'uniform' ]>
Standard uniform distribution. No additional parameters are needed.
=item C<-strategy =E<gt> [ 'Distribution', 'normal', $av, $sd ]>
Normal distribution, where C<$av> is average (default: number of parents/2) and C<$sd> is standard deviation (default: number of parents).
=item C<-strategy =E<gt> [ 'Distribution', 'beta', $aa, $bb ]>
I<Beta> distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
C<$aa> and C<$bb> are set by default to the number of parents.
B<Argument restrictions:> Both $aa and $bb must not be less than 1.0E-37.
=item C<-strategy =E<gt> [ 'Distribution', 'binomial' ]>
Binomial distribution. No additional parameters are needed.
=item C<-strategy =E<gt> [ 'Distribution', 'chi_square', $df ]>
Chi-squared distribution with C<$df> degrees of freedom. C<$df> by default is set to the number of parents.
=item C<-strategy =E<gt> [ 'Distribution', 'exponential', $av ]>
Exponential distribution, where C<$av> is average . C<$av> by default is set to the number of parents.
=item C<-strategy =E<gt> [ 'Distribution', 'poisson', $mu ]>
Poisson distribution, where C<$mu> is mean. C<$mu> by default is set to the number of parents.
=back
=item PMX
PMX method defined by Goldberg and Lingle in 1985. Parameters: I<none>.
=item OX
OX method defined by Davis (?) in 1985. Parameters: I<none>.
=back
=item -cache
This defines whether a cache should be used. Allowed values are 1 or 0
(default: I<0>).
=item -history
This defines whether history should be collected. Allowed values are 1 or 0 (default: I<0>).
=item -native
This defines whether native arrays should be used instead of packing each chromosome into signle scalar.
Turning this option can give you speed up, but much more memory will be used. Allowed values are 1 or 0 (default: I<0>).
=item -mce
This defines whether Many-Core Engine (MCE) should be used during processing.
This can give you significant speed up on many-core/CPU systems, but it'll
increase memory consumption. Allowed values are 1 or 0 (default: I<0>).
=item -workers
This option has any meaning only if MCE is turned on. This defines how
many process will be used during processing. Default will be used one proces per core (most efficient).
=item -strict
This defines if the check for modifying chromosomes in a user-defined fitness
function is active. Directly modifying chromosomes is not allowed and it is
a highway to big trouble. This mode should be used only for testing, because it is B<slow>.
=back
=item I<$ga>-E<gt>B<inject>($chromosomes)
Inject new, user defined, chromosomes into the current population. See example below:
# example for bitvector
my $chromosomes = [
[ 1, 1, 0, 1, 0, 1 ],
[ 0, 0, 0, 1, 0, 1 ],
[ 0, 1, 0, 1, 0, 0 ],
...
];
# inject
$ga->inject($chromosomes);
If You want to delete some chromosomes from population, just C<splice> them:
my @remove = qw(1 2 3 9 12);
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.
=item C<rangevector>
Chromosomes will be lists of values from specified range. See documentation of C<new> method.
=item C<combination>
lib/AI/Genetic/Pro.pm view on Meta::CPAN
[ min0, min1, min2, ... ], # min values
]
=item I<$ga>-E<gt>B<getAvgFitness>()
Get I<max>, I<mean> and I<min> score of the current generation. In example:
my ($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>()
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
Title of a chart (default: I<Evolution>).
=item -x_label
X label (default: I<Generations>).
=item -y_label
Y label (default: I<Value>).
=item -format
Format of values, like C<sprintf> (default: I<'%.2f'>).
=item -legend1
Description of min line (default: I<Min value>).
=item -legend2
Description of min line (default: I<Mean value>).
=item -legend3
Description of min line (default: I<Max value>).
=item -width
Width of a chart (default: I<640>).
=item -height
Height of a chart (default: I<480>).
=item -font
Path to font (in *.ttf format) to be used (default: none).
=item -logo
Path to logo (png/jpg image) to embed in a chart (default: none).
=item For example:
$ga->chart(-width => 480, height => 320, -filename => 'chart.png');
=back
=item I<$ga>-E<gt>B<save>($file)
Save the current state of the genetic algorithm to the specified file.
=item I<$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)
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...
( run in 3.276 seconds using v1.01-cache-2.11-cpan-f56aa216473 )