AI-FANN-Evolving

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lib/AI/FANN/Evolving.pm  view on Meta::CPAN

				my $val = $self->$prop($i,$j);
				$other->$prop($i,$j,$val);			
			}
		}
	}
	return $self;
}

=item recombine

Recombines (exchanges) properties between the two objects at the provided rate, e.g.
$ann1->recombine($ann2,0.5) means that on average half of the object properties are
exchanged between $ann1 and $ann2

=cut

sub recombine {
	my ( $self, $other, $rr ) = @_;
	
	# recombine the simple properties
	my %scalar_properties = __PACKAGE__->_scalar_properties;
	for my $prop ( keys %scalar_properties ) {
		if ( rand(1) < $rr ) {			

lib/AI/FANN/Evolving.pm  view on Meta::CPAN

		train_error_function                 => 'errorfunc',
		train_stop_function                  => 'stopfunc',
		learning_rate                        => \&_mutate_double,
		learning_momentum                    => \&_mutate_double,
		quickprop_decay                      => \&_mutate_double,
		quickprop_mu                         => \&_mutate_double,
		rprop_increase_factor                => \&_mutate_double,
		rprop_decrease_factor                => \&_mutate_double,
		rprop_delta_min                      => \&_mutate_double,
		rprop_delta_max                      => \&_mutate_double,
		cascade_output_change_fraction       => \&_mutate_double,
		cascade_candidate_change_fraction    => \&_mutate_double,
		cascade_output_stagnation_epochs     => \&_mutate_int,
		cascade_candidate_stagnation_epochs  => \&_mutate_int,
		cascade_max_out_epochs               => \&_mutate_int,
		cascade_max_cand_epochs              => \&_mutate_int,
		cascade_num_candidate_groups         => \&_mutate_int,
		bit_fail_limit                       => \&_mutate_double, # 'fann_type',
		cascade_weight_multiplier            => \&_mutate_double, # 'fann_type',
		cascade_candidate_limit              => \&_mutate_double, # 'fann_type',
	)
}

script/aivolver  view on Meta::CPAN

=item B<-e/--experiment <key=valueE<gt>>

The C<experiment> argument is used multiple times, each time followed by a key/value pair
that defines one of the properties of the evolutionary process. The key/value pairs are
as follows:

=over

=item B<crossover_rate=<rateE<gt>>

p of exchange between chromosomes.

=item B<mutation_rate=<rateE<gt>>

p of a trait mutating.

=item B<reproduction_rate=<rateE<gt>>

Proportion of population contributing to next generation.

=item B<ngens=<numberE<gt>>

script/aivolver  view on Meta::CPAN

individuals that each have C<chromosome_count> chromosomes whose C<gene_count> genes
encode the parameters of the ANN. During each generation, each individual is trained
on a sample data set, and the individual's fitness is then calculated by testing its
predictive abilities on an out-of-sample data set. The fittest individuals (whose
fraction of the total is determined by C<reproduction_rate>) are selected for breeding
in proportion to their fitness.

Before breeding, each individual undergoes a process of mutation, where a fraction of
the ANN parameters is randomly perturbed. Both the size of the fraction and the
maximum extent of the perturbation is determined by C<mutation_rate>. Subsequently, the
homologous chromosomes recombine (i.e. exchange parameters) at a rate determined by
C<crossover_rate>, which then results in (haploid) gametes. These gametes are fused with
those of other individuals to give rise to the next generation.

=head1 TRAINING AND TEST DATA

The data that is used for training the ANNs and for subsequently testing their predictive
abilities are provided as tab-separated tables. An example of an input data set is here:

L<https://github.com/naturalis/ai-fann-evolving/blob/master/examples/butterbeetles.tsv>