/*
* 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.ml.ea.opp.selection;
import java.io.Serializable;
import java.util.Random;
import org.encog.mathutil.randomize.RangeRandomizer;
import org.encog.ml.ea.genome.Genome;
import org.encog.ml.ea.species.Species;
import org.encog.ml.ea.train.EvolutionaryAlgorithm;
import org.encog.ml.ea.train.basic.BasicEA;
/**
* Tournament select can be used to select a fit (or unfit) genome from a
* species. The selection is run a set number of rounds. Each round two random
* participants are chosen. The more fit participant continues to the next
* round.
*
* http://en.wikipedia.org/wiki/Tournament_selection
*
*/
public class TournamentSelection implements SelectionOperator, Serializable {
/**
* The serial id.
*/
private static final long serialVersionUID = 1L;
/**
* The trainer being used.
*/
private EvolutionaryAlgorithm trainer;
/**
* The number of rounds.
*/
private int rounds;
/**
* Construct a tournament selection.
* @param theTrainer The trainer.
* @param theRounds The number of rounds to use.
*/
public TournamentSelection(final EvolutionaryAlgorithm theTrainer,
final int theRounds) {
this.trainer = theTrainer;
this.rounds = theRounds;
}
/**
* @return The number of rounds.
*/
public int getRounds() {
return this.rounds;
}
/**
* {@inheritDoc}
*/
@Override
public EvolutionaryAlgorithm getTrainer() {
return this.trainer;
}
/**
* {@inheritDoc}
*/
@Override
public int performAntiSelection(final Random rnd, final Species species) {
int worstIndex = rnd.nextInt(species.getMembers().size());
Genome worst = species.getMembers().get(worstIndex);
BasicEA.calculateScoreAdjustment(worst,
this.trainer.getScoreAdjusters());
for (int i = 0; i < this.rounds; i++) {
final int competitorIndex = rnd.nextInt(species.getMembers().size());
final Genome competitor = species.getMembers().get(competitorIndex);
// force an invalid genome to lose
if (Double.isInfinite(competitor.getAdjustedScore())
|| Double.isNaN(competitor.getAdjustedScore())) {
return competitorIndex;
}
BasicEA.calculateScoreAdjustment(competitor,
this.trainer.getScoreAdjusters());
if (!this.trainer.getSelectionComparator().isBetterThan(competitor,
worst)) {
worst = competitor;
worstIndex = competitorIndex;
}
}
return worstIndex;
}
/**
* {@inheritDoc}
*/
@Override
public int performSelection(final Random rnd, final Species species) {
int bestIndex = rnd.nextInt(species.getMembers().size());
Genome best = species.getMembers().get(bestIndex);
BasicEA.calculateScoreAdjustment(best, this.trainer.getScoreAdjusters());
for (int i = 0; i < this.rounds; i++) {
final int competitorIndex = rnd.nextInt(species.getMembers().size());
final Genome competitor = species.getMembers().get(competitorIndex);
// only evaluate valid genomes
if (!Double.isInfinite(competitor.getAdjustedScore())
&& !Double.isNaN(competitor.getAdjustedScore())) {
BasicEA.calculateScoreAdjustment(competitor,
this.trainer.getScoreAdjusters());
if (this.trainer.getSelectionComparator().isBetterThan(
competitor, best)) {
best = competitor;
bestIndex = competitorIndex;
}
}
}
return bestIndex;
}
/**
* Set the number of rounds.
* @param rounds The number of rounds.
*/
public void setRounds(final int rounds) {
this.rounds = rounds;
}
/**
* Set the trainer.
* @param trainer The trainer.
*/
public void setTrainer(final EvolutionaryAlgorithm trainer) {
this.trainer = trainer;
}
}