view release on metacpan or  search on metacpan

examples/NeuralNet/pso_ann.pl  view on Meta::CPAN

sub test_fitness_function(@) {
    my (@arr) = (@_);
	&writeAnnConfig($annConfig, $numInputs, $numHidden, $xferFunc, @arr);
	my $netValue = &runANN($annConfig, $annInputs);
	print "network value = $netValue\n";

	# the closer the network value gets to our desired value
	# then we want to set the fitness closer to 1.
	#
	# This is a special case of the sigmoid, and looks an awful lot
	# like the hyperbolic tangent ;)
	#
	my $magnitudeFromBest = abs($expectedValue - $netValue);
	return 2 / (1 + exp($magnitudeFromBest));
}

pso_set_params(\%test_params);
pso_register_fitness_function('test_fitness_function');
pso_optimize();
#my @solution = pso_get_solution_array();

lib/AI/PSO.pm  view on Meta::CPAN

  algorithm itself is based off of the emergent behavior among societal 
  groups ranging from marching of ants, to flocking of birds, to 
  swarming of bees.

  PSO is a cooperative approach to optimization rather than an 
  evolutionary approach which kills off unsuccessful members of the 
  search team.  In the swarm framework each particle, is a relatively 
  unintelligent search agent.  It is in the collective sharing of 
  knowledge that solutions are found.  Each particle simply shares its 
  information with its neighboring particles.  So, if one particle is 
  not doing to well (has a low fitness), then it looks to its neighbors 
  for help and tries to be more like them while still maintaining a 
  sense of individuality.

  A particle is defined by its position and velocity.  The parameters a 
  user wants to optimize define the dimensionality of the problem 
  hyperspace.  So, if you want to optimize three variables, a particle 
  will be three dimensional and will have 3 values that devine its 
  position 3 values that define its velocity.  The position of a 
  particle determines how good it is by a user-defined fitness function.  
  The velocity of a particle determines how quickly it changes location.