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        -population
                This defines the size of the population, i.e. how many
                individuals to simultaneously exist at each generation.
                Defaults to 100.

        -crossover
                This defines the crossover rate. Defaults to 0.95.

        -mutation
                This defines the mutation rate. Defaults to 0.05.

        *-fitness*
                This defines a fitness function. It expects a reference to a
                subroutine. More details are given in the section on
                "FITNESS FUNCTION".

        *-type* This defines the type of the genome. Currently, AI::Genetic
                supports only three types:

                *bitvector*
                    Individuals 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 can assume one
                    string value from a specified list of possible string
                    values.

                *rangevector*
                    Each gene of a rangevector individual 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.

                Defaults to *bitvector*.

        *-terminate*
                This option allows the definition of a termination
                subroutine. It expects a subroutine reference. This sub will
                be called at the end of each generation with one argument:
                the AI::Genetic object. Evolution terminates if the sub
                returns a true value.

    *$ga*->createStrategy(*strategy_name*, *sub_ref*)
        This method allows the creation of a custom-made strategy to be used
        during evolution. It expects a unique strategy name, and a
        subroutine reference as arguments. The subroutine will be called
        with one argument: the AI::Genetic object. It is expected to alter
        the population at each generation. See the section on "STRATEGIES"
        for more information.

    *$ga*->init(*initArgs*)
        This method initializes the population with random individuals. It
        MUST be called before any call to *evolve()* or *inject()*. As a
        side effect, any already existing individuals in the population are
        deleted. It expects one argument, which depends on the type of
        individuals:

        o   For bitvectors, the argument is simply the length of the
            bitvector.

                $ga->init(10);

            this initializes a population where each individual has 10
            genes.

        o   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. 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 has 3 genes,
            and each gene can assume one of the given values.

        o   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. 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 has 3 genes,
            and each gene can assume an integer within the corresponding
            range.

    *$ga*->inject(*N*, ?*args*?)
        This method can be used to add more individuals to the population.
        New individuals can be randomly generated, or be explicitly
        specified. The first argument specifies the number, *N*, of new
        individuals to add. This can be followed by at most *N* arguments,
        each of which is an anonymous list that specifies the genome of a
        single individual to add. If the number of genomes given, *n*, is
        less than *N*, then *N* - *n* random individuals are added for a
        total of *N* new individuals. Random individuals are generated using
        the same arguments passed to the *init()* method. For example:

          $ga->inject(5,
                      [qw/red big thin/],
                      [qw/blue small fat/],
                     );

        this adds 5 new individuals, 2 with the specified genetic coding,
        and 3 randomly generated.

    *$ga*->evolve(*strategy*, ?*num_generations*?)
        This method causes the GA to evolve the population using the
        specified strategy. A strategy name has to be specified as the first
        argument. The second argument is optional and specifies the number
        of generations to evolve. It defaults to 1. See the section on