/*
* Copyright 2012 Friedrich Große, Paul Seiferth,
* Sebastian Starroske, Yannik Stein
*
* This file is part of MetaHeuristics4Java.
*
* MetaHeuristics4Java is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* MetaHeuristics4Java is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with MetaHeuristics4Java. If not, see <http://www.gnu.org/licenses/>.
*/
package de.mh4j.solver.genetic.matingselection;
import java.util.ArrayList;
import java.util.Collection;
import java.util.Random;
import de.mh4j.solver.genetic.Couple;
import de.mh4j.solver.genetic.Genome;
import de.mh4j.solver.genetic.genepool.GenePool;
import de.mh4j.util.RNGGenerator;
/**
*
* TODO write class description
*
*/
public class TournamentMatingSelector<GenericGenomeType extends Genome> implements MatingSelector<GenericGenomeType> {
private final Random random;
/**
* TODO write javadoc
*/
public TournamentMatingSelector() {
random = RNGGenerator.createRandomNumberGenerator();
}
/**
* TODO write javadoc
*/
@Override
public Collection<Couple> select(int numberOfPairs, GenePool<GenericGenomeType> genePool) {
Collection<Couple> couples = new ArrayList<Couple>(numberOfPairs);
Genome[] genomes = genePool.toArray();
for (int i = 0; i < numberOfPairs; i++) {
Genome parent1 = selectMate(genomes);
Genome parent2 = selectMate(genomes);
couples.add(new Couple(parent1, parent2));
}
return couples;
}
/**
* TODO write javadoc
*/
protected Genome selectMate(Genome[] genomes) {
Genome bestIndividual = null;
int numberOfIndividualsToDrawForTournament = 1 + random.nextInt(genomes.length - 1);
for (int i = 0; i < numberOfIndividualsToDrawForTournament; i++) {
int indexOfIndividual = random.nextInt(genomes.length);
Genome tmpIndividual = genomes[indexOfIndividual];
if (bestIndividual == null) {
bestIndividual = tmpIndividual;
}
else {
if (bestIndividual.getFitness() < tmpIndividual.getFitness()) {
bestIndividual = tmpIndividual;
}
}
}
return bestIndividual;
}
}