view release on metacpan or  search on metacpan

Genetic.pm  view on Meta::CPAN

this initializes a population where each individual has 10 genes.

=item 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.

=item 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.

=back

=item I<$ga>-E<gt>B<inject>(I<N>, ?I<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, I<N>, of new individuals to add. This can be followed by at most I<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, I<n>, is less than I<N>, then
I<N> - I<n> random individuals are added for a total of I<N> new individuals. Random
individuals are generated using the same arguments passed to the I<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.

=item I<$ga>-E<gt>B<evolve>(I<strategy>, ?I<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 L</"STRATEGIES"> for more information on the default strategies.

Each generation consists of the following steps:

=over

=item o

The population is sorted according to the individuals' fitnesses.

=item o

The subroutine corresponding to the named strategy is called with one argument,
the AI::Genetic object. This subroutine is expected to alter the object itself.

=item o

If a termination subroutine is given, it is executed and the return value is
checked. Evolution terminates if this sub returns a true value.

=back

=item I<$ga>-E<gt>B<getFittest>(?I<N>?)

This returns the I<N> fittest individuals. If not specified,
I<N> defaults to 1. As a side effect, it sorts the population by
fitness score. The actual AI::Genetic::Individual objects are returned.
You can use the C<genes()> and C<score()> methods to get the genes and the
scores of the individuals. Please check L<AI::Genetic::Individual> for details.

=item I<$ga>-E<gt>B<sortPopulation>

This method sorts the population according to fitness function. The results
are cached for speed.

=item I<$ga>-E<gt>B<sortIndividuals>(?[I<ListOfIndividuals>]?)

Given an anonymous list of individuals, this method sorts them according
to fitness, returning an anonymous list of the sorted individuals.

=item I<$ga>-E<gt>B<people>()

Returns an anonymous list of individuals of the current population.
B<IMPORTANT>: the actual array reference used by the AI::Genetic object
is returned, so any changes to it will be reflected in I<$ga>.

=item I<$ga>-E<gt>B<size>(?I<newSize>?)

This method is used to query and set the population size.

=item I<$ga>-E<gt>B<crossProb>(?I<newProb>?)

This method is used to query and set the crossover rate.

=item I<$ga>-E<gt>B<mutProb>(?I<newProb>?)

This method is used to query and set the mutation rate.

=item I<$ga>-E<gt>B<indType>()

This method returns the type of individual: I<bitvector>, I<listvector>,
or I<rangevector>.

=item I<$ga>-E<gt>B<generation>()

This method returns the current generation.

=back

=head1 FITNESS FUNCTION

Very quickly you will realize that properly defining the fitness function
is the most important aspect of a GA. Most of the time that a genetic
algorithm takes to run is spent in running the fitness function for each
separate individual to get its fitness. AI::Genetic tries to minimize this
time by caching the fitness result for each individual. But, B<you should
spend a lot of time optimizing your fitness function to achieve decent run
times.>

The fitness function should expect only one argument, an anonymous list of
genes, corresponding to the individual being analyzed. It is expected
to return a number which defines the fitness score of the said individual.
The higher the score, the more fit the individual, the more the chance it
has to be chosen for crossover.

=head1 STRATEGIES