NAME AI::Genetic::Pro - Efficient genetic algorithms for professional purpose. 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 individuals/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 ); # 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 $ga->load('genetic.sga'); DESCRIPTION This module provides efficient implementation of a genetic algorithm for a professional purpose. It was designed to operate as fast as possible even on very large populations and big individuals/chromosomes. "AI::Genetic::Pro" was inspired by "AI::Genetic", so it is in most cases compatible (there are some changes). Additionaly "AI::Genetic::Pro" isn't pure Perl solution, so it doesn't have limitations of its ancestor (ie. seriously slow down in case of big populations ( >10000 ) or vectors with size > 33 fields). If You are looking for pure Perl solution, consider AI::Genetic. Speed To increase speed XS code are used, however with portability in mind. This distribution was tested on Windows and Linux platforms (should work on any other). Memory This module was designed to use as little memory as possible. Population of size 10000 consist 92-bit vectors uses only ~24MB (in "AI::Genetic" something about ~78MB!!!). Advanced options To provide more flexibility "AI::Genetic::Pro" supports many statistic distributions, such as: "uniform", "natural", "chi_square" and others. This feature can be used in selection or/and crossover. See documentation below. METHODS Simply description of available methods. See below. *$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 to simultaneously exist at each generation. -crossover This defines the crossover rate. Fairest results are achieved with crossover rate ~0.95. -mutation This defines the mutation rate. Fairest results are achieved with mutation rate ~0.01. -parents This defines how many parents should used in corssover. -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 poportionaly to its fitness. Roulette At the first best individuals/chromosomes are selected. From this collection parents are selected with probability poportionaly to its fitness. RouletteDistribution Each individual/chromosome has portion of roulette wheel proportionaly to its fitness. Selection is done with specified distribution. Supported distributions and paremeters 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 strategy 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. Best chromosomes/individuals are selected to new generation. In example: -strategy => [ 'PointsSimple', $n ] where $n is number of points for crossing. PointsBasic Crossover in one or many points. In basic crossover selected parents are crossed and one (random) of children is moved to new generation. In example: -strategy => [ 'PointsBasic', $n ] where $n is number of points for crossing. Points Crossover in one or many points. In normal crossover selected parents are crossed and the best of child is moved to new generation. In example: -strategy => [ 'Points', $n ] where $n is number of points for crossing. PointsAdvenced Crossover in one or many points. After crossover best chromosomes/individuals from all parents and chidren are selected to new generation. In example: -strategy => [ 'PointsAdvanced', $n ] where $n is number of points for crossing. Distribution In *distribution* crossover parents are crossed in points selected with 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: size of population /2) and $$sd is standard deviation (default: size of population). "-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 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-square distribution with $df degrees of freedom. $df by default is set to size of population. "-strategy => [ 'Distribution', 'exponential', $av ]" Exponential distribution, where $av is average . $av by default is set to size of population. "-strategy => [ 'Distribution', 'poisson', $mu ]" Poisson distribution, where $mu is mean. $mu by default is set to size of population. PMX PMX method defined by Goldberg and Lingle in 1985. Parameters: *none*. OX OX method defined by Davis (?) in 1985. Parameters: *none*. -cache This defines if cache should be used. Correct values are: 1 or 0 (default: *0*). -history This defines if history should be collected. Correct values are: 1 or 0 (default: *0*). Collect history. *$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 uniq lists of specified values. This is used for example in *Traveling Salesman Problem*. See documentation of "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 "mutation". *$ga*->parents($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. $ga->init( [ 'a', 'b', 'c' ] ); This initializes a population where each chromosome has 3 genes and each gene is uniq combination of 'a', 'b' and 'c'. For example genes looks something like that: [ 'a', 'b', 'c' ] # gene 1 [ 'c', 'a', 'b' ] # gene 2 [ 'b', 'c', 'a' ] # gene 3 # ...and so on... *$ga*->evolve() This method causes the GA to evolve the population for the specified number of generations. *$ga*->getHistory() Get history of the evolution. It is in a format listed below: [ # gen0 gen1 gen2 ... # generations [ max0, max1, max2, ... ], # max values [ mean, mean1, mean2, ... ], # mean values [ min0, min1, min2, ... ], # min values ] *$ga*->getAvgFitness() Get *max*, *mean* and *min* score of the current generation. In example: my ($max, $mean, $min) = $ga->getAvgFitness(); *$ga*->getFittest() Get fittest chromosome. *$ga*->generation() Get number of generation. *$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, max scores in Your population. To satisfy Your needs, You can pass 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). In example: $ga->chart(-width => 480, height => 320, -filename => 'chart.png'); *$ga*->save($file) Save current state of the genetic algorithm to a specified file. *$ga*->load($file) Load a state of the genetic algorithm from a specified file. *$ga*->as_array($chromosome) Return an array representing specified chromosome. *$ga*->as_value($chromosome) Return score of specified chromosome. Value of *chromosome* is calculated by fitness function. *$ga*->as_string($chromosome) Return string-representation of specified chromosome. DOCUMENTATION This documentation is still incomplete, however it is based on POD of AI::Genetic. So if You are in a trouble, try to take a look to the documentation of AI::Genetic. SUPPORT "AI::Genetic::Pro" is still under development and it has poor documentation (for now). However it is used in many production environments. TODO Examples. More tests. Warnings in case of 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 Again... Alec Chen for reporting a bug :-) Alec Chen for reporting a bug. AUTHOR Strzelecki Lukasz SEE ALSO AI::Genetic 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.