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NAME
    AI::Genetic - A pure Perl genetic algorithm implementation.

SYNOPSIS
        use AI::Genetic;
        my $ga = new AI::Genetic(
            -fitness    => \&fitnessFunc,
            -type       => 'bitvector',
            -population => 500,
            -crossover  => 0.9,
            -mutation   => 0.01,
            -terminate  => \&terminateFunc,
           );

         $ga->init(10);
         $ga->evolve('rouletteTwoPoint', 100);
         print "Best score = ", $ga->getFittest->score, ".\n";

         sub fitnessFunc {
             my $genes = shift;

             my $fitness;
             # assign a number to $fitness based on the @$genes
             # ...

             return $fitness;
          }

          sub terminateFunc {
             my $ga = shift;

             # terminate if reached some threshold.
             return 1 if $ga->getFittest->score > $THRESHOLD;
             return 0;
          }

DESCRIPTION
    This module implements a Genetic Algorithm (GA) in pure Perl. Other Perl
    modules that achieve the same thing (perhaps better, perhaps worse) do
    exist. Please check CPAN. I mainly wrote this module to satisfy my own
    needs, and to learn something about GAs along the way.

    PLEASE NOTE: As of v0.02, AI::Genetic has been re-written from scratch
    to be more modular and expandable. To achieve this, I had to modify the
    API, so it is not backward-compatible with v0.01. As a result, I do not
    plan on supporting v0.01.

    I will not go into the details of GAs here, but here are the bare
    basics. Plenty of information can be found on the web.

    In a GA, a population of individuals compete for survival. Each
    individual is designated by a set of genes that define its behaviour.
    Individuals that perform better (as defined by the fitness function)
    have a higher chance of mating with other individuals. When two
    individuals mate, they swap some of their genes, resulting in an
    individual that has properties from both of its "parents". Every now and
    then, a mutation occurs where some gene randomly changes value,
    resulting in a different individual. If all is well defined, after a few
    generations, the population should converge on a "good-enough" solution
    to the problem being tackled.

    A GA implementation runs for a discrete number of time steps called
    *generations*. What happens during each generation can vary greatly
    depending on the strategy being used (See the section on "STRATEGIES"
    for more info). Typically, a variation of the following happens at each
    generation:

    1. Selection
        Here the performance of all the individuals is evaluated based on
        the fitness function, and each is given a specific fitness value.
        The higher the value, the bigger the chance of an individual passing
        its genes on in future generations through mating (crossover).

    2. Crossover
        Here, individuals selected are randomly paired up for crossover (aka
        *sexual reproduction*). This is further controlled by the crossover
        rate specified and may result in a new offspring individual that