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
            -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');

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. "AI::Genetic::Pro"
    was inspired by "AI::Genetic", so it is in most cases compatible (there
    are some changes). Additionaly "AI::Genetic::Pro" doesn't have
    limitations of its ancestor (ie. seriously slow down in case of big
    populations ( >10000 ) or vectors with size > 33 fields).

    *   To provide more flexibility "AI::Genetic::Pro" supports many
        statistic distributions, such as: "uniform", "natural", "chi_square"
        and others.

    *   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).

    *   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!!!).

METHODS
    "new( { param =" value, param0 => value0 } )>
        Constructor.

    "population($population)"
        Set/get population.

    "type($type)"
        Set/get type of individuals. Currently it can be set to:

        bitvector,
        listvector,
        rangevector,
        combination.

    "parents($parents)"
        Set/get number of parents in *Roulette* crossover.

    "init()"
    "evolve()"
    "history()"
    "getAvgFitness()"
    "getFittest()"
    "getHistory()"
    "generation()"
    "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 in chart (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*),
        *-font* - path to font in (*.ttf format) to be used (default: none),
        *-logo* - path to logo (png/jpg image) to embed in a chart,
        *-width* - width of a chart,
        *-height* - height of a chart,

        In example:

                $ga->chart(-width => 480, height => 320, -filename => 'chart.png');

    "save($file)"
        Save current state of the genetic algorithm to a specified file.

    "load($file)"
        Load a state of the genetic algorithm from a specified file.

    "as_array($chromosome)"
        Return an array representing specified chromosome.

    "as_value($chromosome)"
        Return score of specified chromosome. Value of *chromosome* is
        calculated by fitness function.

    "as_string($chromosome)"
        Return string-representation of specified chromosome.

DOCUMENTATION
    At the moment for more information see documentation of AI::Genetic. It
    is compatible in most cases.

SUPPORT
    "AI::Genetic::Pro" is still under development and it has very poor
    documentation. However it is used in many production environments.

TODO
    More documentation.
    More tests.
    Warnings.

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.

AUTHOR
    Strzelecki Łukasz <strzelec@rswsystems.com>

SEE ALSO
    AI::Genetic

COPYRIGHT
    Copyright (c) Strzelecki Łukasz. All rights reserved. This program is
    free software; you can redistribute it and/or modify it under the same
    terms as Perl itself.