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 <strzelec@rswsystems.com>

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.

