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	'env'       => $testdata->to_fann,
	'workdir'   => $wd,
	%experiment,
);

# initialize the experiment
$exp->initialize(%initialize);

# run!
my ( $fittest, $fitness ) = $exp->run();
$log->info("*** overall best fitness: $fitness");
my ($gene) = sort { $a->fitness <=> $b->fitness } map { $_->genes } $fittest->chromosomes;
$gene->ann->save($outfile);

__END__

=pod

=head1 NAME

aivolver - Evolves optimal artificial neural networks

script/aivolver  view on Meta::CPAN

Artificial neural networks (ANNs) are decision-making machines that develop their
capabilities by training on input data. During this training, the ANN builds a
topology of input neurons, hidden neurons, and output neurons that respond to signals
in ways (and with sensitivities) that are determined by a variety of parameters. How
these parameters will interact to give rise to the final functionality of the ANN is
hard to predict I<a priori>, but can be optimized in a variety of ways.

C<aivolver> is a program that does this by evolving parameter settings using a genetic
algorithm that runs for a number of generations determined by C<ngens>. During this
process it writes the intermediate ANNs into the C<workdir> until the best result is
written to the C<outfile>.

The genetic algorithm proceeds by simulating a population of C<individual_count> diploid
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.