AI-Genetic-Pro
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NAME
AI::Genetic::Pro - Efficient genetic algorithms for professional
purpose with support for multiprocessing.
SYNOPSIS
use AI::Genetic::Pro;
sub fitness {
my ($ga, $chromosome) = @_;
return oct('0b' . $ga->as_string($chromosome));
}
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
-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');
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
should work on any other).
Multicore support is available through Many-Core Engine (MCE). You
can gain the most speed up for big populations or time/CPU consuming
fitness functions, however for small populations and/or simple
fitness function better choice will be single-process version.
You can get even more speed up if you turn on use of native arrays
(parameter: native) instead of packing chromosomes into single
scalar. However you have to remember about expensive memory use in
that case.
Memory
This module was designed to use as little memory as possible. A
population of size 10000 consisting of 92-bit vectors uses only ~24MB
(AI::Genetic would use about 78MB). However - if you use MCE - there
will be bigger memory consumption. This is consequence of necessity
of synchronization between many processes.
Advanced options
To provide more flexibility AI::Genetic::Pro supports many
statistical distributions, such as uniform, natural, chi_square and
others. This feature can be used in selection and/or crossover. See
the documentation below.
METHODS
$ga->new( %options )
Constructor. It accepts options in hash-value style. See options and
an example below.
-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
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.
combination
Each gene of a "combination" individual/chromosome can assume one
string value from a specified list of possible string values. All
genes are unique.
-population
This defines the size of the population, i.e. how many chromosomes
simultaneously exist at each generation.
-crossover
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
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->as_array($chromosome)) can have 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 undef or space,
just use as_array_def_only and as_string_def_only instead of
as_array and as_string.
-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
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.
-selection => [ 'RouletteDistribution', 'uniform' ]
Standard uniform distribution. No additional parameters are
needed.
-selection => [ 'RouletteDistribution', 'normal', $av, $sd ]
Normal distribution, where $av is average (default: size of
population /2) and $$sd is standard deviation (default: size of
population).
-selection => [ 'RouletteDistribution', 'beta', $aa, $bb ]
Beta distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
$aa and $bb are set by default to number of parents.
Argument restrictions: Both $aa and $bb must not be less than
1.0E-37.
-selection => [ 'RouletteDistribution', 'binomial' ]
Binomial distribution. No additional parameters are needed.
-selection => [ 'RouletteDistribution', 'chi_square', $df ]
Chi-square distribution with $df degrees of freedom. $df by
default is set to size of population.
-selection => [ 'RouletteDistribution', 'exponential', $av ]
Exponential distribution, where $av is average . $av by default
is set to size of population.
-selection => [ 'RouletteDistribution', 'poisson', $mu ]
Poisson distribution, where $mu is mean. $mu by default is set
to size of population.
Distribution
Chromosomes/individuals are selected with specified distribution.
See below.
-selection => [ 'Distribution', 'uniform' ]
Standard uniform distribution. No additional parameters are
needed.
-selection => [ 'Distribution', 'normal', $av, $sd ]
Normal distribution, where $av is average (default: size of
population /2) and $$sd is standard deviation (default: size of
population).
-selection => [ 'Distribution', 'beta', $aa, $bb ]
Beta distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
$aa and $bb are set by default to number of parents.
Argument restrictions: Both $aa and $bb must not be less than
1.0E-37.
-selection => [ 'Distribution', 'binomial' ]
Binomial distribution. No additional parameters are needed.
-selection => [ 'Distribution', 'chi_square', $df ]
Chi-square distribution with $df degrees of freedom. $df by
default is set to size of population.
-selection => [ 'Distribution', 'exponential', $av ]
Exponential distribution, where $av is average . $av by default
is set to size of population.
-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.
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
with the specified distribution. See below.
-strategy => [ 'Distribution', 'uniform' ]
Standard uniform distribution. No additional parameters are
needed.
-strategy => [ 'Distribution', 'normal', $av, $sd ]
Normal distribution, where $av is average (default: number of
parents/2) and $sd is standard deviation (default: number of
parents).
-strategy => [ 'Distribution', 'beta', $aa, $bb ]
Beta distribution. The density of the beta is:
X^($aa - 1) * (1 - X)^($bb - 1) / B($aa , $bb) for 0 < X < 1.
$aa and $bb are set by default to the number of parents.
Argument restrictions: Both $aa and $bb must not be less than
1.0E-37.
-strategy => [ 'Distribution', 'binomial' ]
Binomial distribution. No additional parameters are needed.
-strategy => [ 'Distribution', 'chi_square', $df ]
Chi-squared distribution with $df degrees of freedom. $df by
default is set to the number of parents.
-strategy => [ 'Distribution', 'exponential', $av ]
Exponential distribution, where $av is average . $av by default
is set to the number of parents.
-strategy => [ 'Distribution', 'poisson', $mu ]
Poisson distribution, where $mu is mean. $mu by default is set
to the number of parents.
PMX
PMX method defined by Goldberg and Lingle in 1985. Parameters:
none.
OX
OX method defined by Davis (?) in 1985. Parameters: none.
-cache
This defines whether a cache should be used. Allowed values are 1
or 0 (default: 0).
-history
This defines whether history should be collected. Allowed values
are 1 or 0 (default: 0).
-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: 0).
-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: 0).
-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).
-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 slow.
$ga->inject($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 splice
them:
my @remove = qw(1 2 3 9 12);
for my $idx (sort { $b <=> $a } @remove){
splice @{$ga->chromosomes}, $idx, 1;
}
$ga->population($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.
rangevector
Chromosomes will be lists of values from specified range. See
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.
$ga->mutProb()
This method is used to query and set the mutation rate.
$ga->mutation()
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.
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([
[qw/red blue green/],
[qw/big medium small/],
[qw/very_fat fat fit thin very_thin/],
]);
This initializes a population where each individual/chromosome has
3 genes and each gene can assume one of the given values.
rangevector
For rangevectors, 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 minimum and
maximum integer values that the corresponding gene can assume.
$ga->init([
[1, 5],
[0, 20],
[4, 9],
]);
This initializes a population where each individual/chromosome has
3 genes and each gene can assume an integer within the
corresponding range.
combination
For combination, the argument is an anonymous list of possible
values of gene.
$ga->init( [ 'a', 'b', 'c' ] );
This initializes a population where each chromosome has 3 genes and
each gene is a unique combination of 'a', 'b' and 'c'. For example
genes looks something like that:
[ 'a', 'b', 'c' ] # gene 1
[ 'c', 'a', 'b' ] # gene 2
[ 'b', 'c', 'a' ] # gene 3
# ...and so on...
$ga->evolve($n)
This method causes the GA to evolve the population for the specified
number of generations. If its argument is 0 or undef GA will evolve
the population to infinity unless a terminate function is specified.
$ga->getHistory()
Get history of the evolution. It is in a format listed below:
[
# gen0 gen1 gen2 ... # generations
[ max0, max1, max2, ... ], # max values
[ mean, mean1, mean2, ... ], # mean values
[ min0, min1, min2, ... ], # min values
]
$ga->getAvgFitness()
Get max, mean and min score of the current generation. In example:
my ($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()
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).
-x_label
X label (default: Generations).
-y_label
Y label (default: Value).
-format
Format of values, like sprintf (default: '%.2f').
-legend1
Description of min line (default: Min value).
-legend2
Description of min line (default: Mean value).
-legend3
Description of min line (default: Max value).
-width
Width of a chart (default: 640).
-height
Height of a chart (default: 480).
-font
Path to font (in *.ttf format) to be used (default: none).
-logo
Path to logo (png/jpg image) to embed in a chart (default: none).
For example:
$ga->chart(-width => 480, height => 320, -filename => 'chart.png');
$ga->save($file)
Save the current state of the genetic algorithm to the specified
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)
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)
Return the score of the specified chromosome. The value of chromosome
is calculated by the fitness function.
SUPPORT
AI::Genetic::Pro is still under development; however, it is used in
many production environments.
TODO
Examples.
More tests.
More warnings about incorrect parameters.
REPORTING BUGS
When reporting bugs/problems please include as much information as
possible. It may be difficult for me to reproduce the problem as almost
every setup is different.
A small script which yields the problem will probably be of help.
THANKS
Mario Roy for suggestions about efficiency.
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 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>
SEE ALSO
AI::Genetic Algorithm::Evolutionary
COPYRIGHT
Copyright (c) Strzelecki Lukasz. All rights reserved. This program is
free software; you can redistribute it and/or modify it under the same
terms as Perl itself.
( run in 0.247 second using v1.01-cache-2.11-cpan-4d50c553e7e )