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        # 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