/*
* Encog(tm) Core v3.4 - Java Version
* http://www.heatonresearch.com/encog/
* https://github.com/encog/encog-java-core
* Copyright 2008-2016 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.io.Serializable;
import java.util.ArrayList;
import java.util.Collections;
import java.util.List;
import java.util.Random;
import org.encog.ml.ea.genome.Genome;
import org.encog.ml.ea.opp.EvolutionaryOperator;
import org.encog.ml.ea.train.EvolutionaryAlgorithm;
import org.encog.neural.neat.NEATGenomeFactory;
import org.encog.neural.neat.training.NEATGenome;
import org.encog.neural.neat.training.NEATLinkGene;
import org.encog.neural.neat.training.NEATNeuronGene;
/**
* Crossover is performed by mixing the link genes between the parents to
* produce an offspring. Only the link genes are considered for crossover. The
* neuron genes are chosen by virtue of which link genes were chosen. If a
* neuron gene is present in both parents, then we choose the neuron gene from
* the more fit of the two parents.
*
* For NEAT, it does not really matter what parent we get the neuron gene from.
* However, because HyperNEAT also encodes a unique activation function into the
* neuron, the selection of a neuron gene between two parents is more important.
*
* The crossover operator defines two broad classes of genes. Matching genes are
* those genes that are present in both parents. Non-matching genes are only
* present in one person. Non-matching genes are further divided into two more
* groups:
*
* disjoint genes: Genes in the middle of a genome that do not match between the
* parents. excess genes: Genes at the edge of a genome that do not match
* between the parents.
*
* Matching genes are inherited randomly, whereas disjoint genes (those that do
* not match in the middle) and excess genes (those that do not match in the
* end) are inherited from the more fit parent. In this case, equal fitnesses
* are assumed, so the disjoint and excess genes are also inherited randomly.
* The disabled genes may become enabled again in future generations: there is a
* preset chance that an inherited gene is disabled if it is disabled in either
* parent.
*
* This is implemented in this class via the following algorithm. First, create
* a counter for each parent. At each step in the loop, perform the following.
*
* If both parents have the same innovation number, then randomly choose which
* parent's gene to use. Increase the parent counter who contributed the gene.
* Else if one parent has a lower innovation number than the other, then include
* the lower innovation gene if its parent is the most fit. Increase the parent
* counter who contributed the gene.
*
* -----------------------------------------------------------------------------
* 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 NEATCrossover implements EvolutionaryOperator, Serializable {
/**
* The owning object.
*/
private EvolutionaryAlgorithm owner;
/**
* Add a neuron.
* @param nodeID The ID.
* @param vec The list of ID's used.
* @param best The best node.
* @param notBest Not the best node.
*/
public void addNeuronID(final long nodeID, final List vec,
final NEATGenome best, final NEATGenome notBest) {
for (int i = 0; i < vec.size(); i++) {
if (vec.get(i).getId() == nodeID) {
return;
}
}
vec.add(findBestNeuron(nodeID, best, notBest));
return;
}
/**
* Choose a parent to favor.
*
* @param rnd
* A random number generator.
* @param mom
* The mother.
* @param dad
* The father.
* @return The parent to favor.
*/
private NEATGenome favorParent(final Random rnd, final NEATGenome mom,
final NEATGenome dad) {
// first determine who is more fit, the mother or the father?
// see if mom and dad are the same fitness
if (mom.getScore() == dad.getScore()) {
// are mom and dad the same fitness
if (mom.getNumGenes() == dad.getNumGenes()) {
// if mom and dad are the same fitness and have the same number
// of genes,
// then randomly pick mom or dad as the most fit.
if (rnd.nextDouble() < 0.5) {
return mom;
} else {
return dad;
}
}
// mom and dad are the same fitness, but different number of genes
// favor the parent with fewer genes
else {
if (mom.getNumGenes() < dad.getNumGenes()) {
return mom;
} else {
return dad;
}
}
} else {
// mom and dad have different scores, so choose the better score.
// important to note, better score COULD BE the larger or smaller
// score.
if (this.owner.getSelectionComparator().compare(mom, dad) < 0) {
return mom;
}
else {
return dad;
}
}
}
/**
* Find the best neuron, between two parents by the specified neuron id.
*
* @param nodeID
* The neuron id.
* @param best
* The best genome.
* @param notBest
* The non-best (second best) genome. Also the worst, since this
* is the 2nd best of 2.
* @return The best neuron genome by id.
*/
private NEATNeuronGene findBestNeuron(final long nodeID,
final NEATGenome best, final NEATGenome notBest) {
NEATNeuronGene result = best.findNeuron(nodeID);
if (result == null) {
result = notBest.findNeuron(nodeID);
}
return result;
}
/**
* Init this operator. This allows the EA to be defined.
*/
@Override
public void init(final EvolutionaryAlgorithm theOwner) {
this.owner = theOwner;
}
/**
* {@inheritDoc}
*/
@Override
public int offspringProduced() {
return 1;
}
/**
* {@inheritDoc}
*/
@Override
public int parentsNeeded() {
return 2;
}
/**
* {@inheritDoc}
*/
@Override
public void performOperation(final Random rnd, final Genome[] parents,
final int parentIndex, final Genome[] offspring,
final int offspringIndex) {
final NEATGenome mom = (NEATGenome) parents[parentIndex + 0];
final NEATGenome dad = (NEATGenome) parents[parentIndex + 1];
final NEATGenome best = favorParent(rnd, mom, dad);
final NEATGenome notBest = (best != mom) ? mom : dad;
final List selectedLinks = new ArrayList();
final List selectedNeurons = new ArrayList();
int curMom = 0; // current gene index from mom
int curDad = 0; // current gene index from dad
NEATLinkGene selectedGene = null;
// add in the input and bias, they should always be here
final int alwaysCount = ((NEATGenome)parents[0]).getInputCount()
+ ((NEATGenome)parents[0]).getOutputCount() + 1;
for (int i = 0; i < alwaysCount; i++) {
addNeuronID(i, selectedNeurons, best, notBest);
}
while ((curMom < mom.getNumGenes()) || (curDad < dad.getNumGenes())) {
NEATLinkGene momGene = null; // the mom gene object
NEATLinkGene dadGene = null; // the dad gene object
long momInnovation = -1;
long dadInnovation = -1;
// grab the actual objects from mom and dad for the specified
// indexes
// if there are none, then null
if (curMom < mom.getNumGenes()) {
momGene = mom.getLinksChromosome().get(curMom);
momInnovation = momGene.getInnovationId();
}
if (curDad < dad.getNumGenes()) {
dadGene = dad.getLinksChromosome().get(curDad);
dadInnovation = dadGene.getInnovationId();
}
// now select a gene for mom or dad. This gene is for the baby
if ((momGene == null) && (dadGene != null)) {
if (best == dad) {
selectedGene = dadGene;
}
curDad++;
} else if ((dadGene == null) && (momGene != null)) {
if (best == mom) {
selectedGene = momGene;
}
curMom++;
} else if (momInnovation < dadInnovation) {
if (best == mom) {
selectedGene = momGene;
}
curMom++;
} else if (dadInnovation < momInnovation) {
if (best == dad) {
selectedGene = dadGene;
}
curDad++;
} else if (dadInnovation == momInnovation) {
if (Math.random() < 0.5f) {
selectedGene = momGene;
}
else {
selectedGene = dadGene;
}
curMom++;
curDad++;
}
if (selectedGene != null) {
if (selectedLinks.size() == 0) {
selectedLinks.add(selectedGene);
} else {
if (selectedLinks.get(selectedLinks.size() - 1)
.getInnovationId() != selectedGene
.getInnovationId()) {
selectedLinks.add(selectedGene);
}
}
// Check if we already have the nodes referred to in
// SelectedGene.
// If not, they need to be added.
addNeuronID(selectedGene.getFromNeuronID(), selectedNeurons,
best, notBest);
addNeuronID(selectedGene.getToNeuronID(), selectedNeurons,
best, notBest);
}
}
// now create the required nodes. First sort them into order
Collections.sort(selectedNeurons);
// finally, create the genome
final NEATGenomeFactory factory = (NEATGenomeFactory) this.owner
.getPopulation().getGenomeFactory();
final NEATGenome babyGenome = factory.factor(selectedNeurons,
selectedLinks, mom.getInputCount(), mom.getOutputCount());
babyGenome.setBirthGeneration(this.owner.getIteration());
babyGenome.setPopulation(this.owner.getPopulation());
babyGenome.sortGenes();
offspring[offspringIndex] = babyGenome;
}
}
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