AI-Genetic

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Genetic.pm  view on Meta::CPAN

  return $r[0] if $N == 1 && not wantarray;

  return @r;
}

# sub init():
# This method initializes the population to completely
# random individuals. It deletes all current individuals!!!
# It also examines the type of individuals we want, and
# require()s the proper class. Throws an error if it can't.
# Must pass to it an anon list that will be passed to the
# newRandom method of the individual.

# In case of bitvector, $newArgs is length of bitvector.
# In case of rangevector, $newArgs is anon list of anon lists.
# each sub-anon list has two elements, min number and max number.
# In case of listvector, $newArgs is anon list of anon lists.
# Each sub-anon list contains possible values of gene.

sub init {

Genetic.pm  view on Meta::CPAN

# sub inject():
# This method is used to add individuals to the current population.
# The point of it is that sometimes the population gets stagnant,
# so it could be useful add "fresh blood".
# Takes a variable number of arguments. The first argument is the
# total number, N, of new individuals to add. The remaining arguments
# are genomes to inject. There must be at most N genomes to inject.
# If the number, n, of genomes to inject is less than N, N - n random
# genomes are added. Perhaps an example will help?
# returns 1 on success and undef on error.

sub inject {
  my ($self, $count, @genomes) = @_;

  unless ($self->{INIT}) {
    carp "can't inject() before init()";
    return undef;
  }

  my $ind = $self->{INDIVIDUAL};

Genetic/Individual.pm  view on Meta::CPAN

  return $self;
}

# should create default methods.
# those are the only three needed.
sub newRandom   {}
sub newSpecific {}
sub genes       {}

# the following methods shouldn't be overridden.
sub fitness {
  my ($self, $sub) = @_;

  $self->{FITFUNC} = $sub if $sub;
  return $self->{FITFUNC};
}

sub score {
  my $self = shift;

Genetic/Individual.pm  view on Meta::CPAN

This method returns an anonymous list of lists which describes the possible
range of each gene for the given AI::Genetic::IndRangeVector individual. This
is the same argument passed to I<newRandom()>.

=back

=head1 CREATING YOUR OWN INDIVIDUAL CLASS

Creating your own individual class is easy. All you have to do is inherit from
AI::Genetic::Individual and override the I<newRandom()>, I<newSpecific>, and
I<genes()> methods to conform with the documentation above. Specifically, the
arguments to i<newRandom> and I<newSpecific> have to match what I<AI::Genetic::init()>
expects as arguments. You can also define any additional methods that you might
require in your own custom-made strategies.

Note that in order for your own individual class to be useful, you have to define
your own custom strategy that knows how to evolve such individuals. Conceptually,
this should be very simple.

=head1 AUTHOR