/*
* Encog(tm) Core v3.3 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2014 Heaton Research, Inc.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* For more information on Heaton Research copyrights, licenses
* and trademarks visit:
* http://www.heatonresearch.com/copyright
*/
package org.encog.neural.neat.training.opp;
import java.util.Random;
import org.encog.mathutil.randomize.RangeRandomizer;
import org.encog.ml.ea.genome.Genome;
import org.encog.neural.neat.NEATNeuronType;
import org.encog.neural.neat.NEATPopulation;
import org.encog.neural.neat.training.NEATGenome;
import org.encog.neural.neat.training.NEATNeuronGene;
/**
* Mutates a NEAT genome by adding a link. To add a link, two random neurons are
* chosen and a new random link is created between them. There are severals
* rules. Bias and input neurons can never be the target of a link. We also do
* not create double links. Two neurons cannot have more than one link going in
* the same direction. A neuron can have a single link to itself.
*
* If the network is only one cycle, then output neurons cannot be a target.
*
* -----------------------------------------------------------------------------
* http://www.cs.ucf.edu/~kstanley/ Encog's NEAT implementation was drawn from
* the following three Journal Articles. For more complete BibTeX sources, see
* NEATNetwork.java.
*
* Evolving Neural Networks Through Augmenting Topologies
*
* Generating Large-Scale Neural Networks Through Discovering Geometric
* Regularities
*
* Automatic feature selection in neuroevolution
*/
public class NEATMutateAddLink extends NEATMutation {
/**
* {@inheritDoc}
*/
@Override
public void performOperation(Random rnd, Genome[] parents, int parentIndex,
Genome[] offspring, int offspringIndex) {
int countTrysToAddLink = this.getOwner().getMaxTries();
NEATGenome target = this.obtainGenome(parents, parentIndex, offspring,
offspringIndex);
// the link will be between these two neurons
long neuron1ID = -1;
long neuron2ID = -1;
// try to add a link
while ((countTrysToAddLink--) > 0) {
final NEATNeuronGene neuron1 = chooseRandomNeuron(target, true);
final NEATNeuronGene neuron2 = chooseRandomNeuron(target, false);
if (neuron1 == null || neuron2 == null) {
return;
}
// do not duplicate
// do not go to a bias neuron
// do not go from an output neuron
// do not go to an input neuron
if (!isDuplicateLink(target, neuron1.getId(), neuron2.getId())
&& (neuron2.getNeuronType() != NEATNeuronType.Bias)
&& (neuron2.getNeuronType() != NEATNeuronType.Input)) {
if ( ((NEATPopulation)getOwner().getPopulation()).getActivationCycles() != 1
|| neuron1.getNeuronType() != NEATNeuronType.Output) {
neuron1ID = neuron1.getId();
neuron2ID = neuron2.getId();
break;
}
}
}
// did we fail to find a link
if ((neuron1ID < 0) || (neuron2ID < 0)) {
return;
}
double r = ((NEATPopulation) target.getPopulation()).getWeightRange();
createLink(target, neuron1ID, neuron2ID,
RangeRandomizer.randomize(rnd, -r, r));
target.sortGenes();
}
}