TournamentSelector.java
/*
* Java Genetic Algorithm Library (@__identifier__@).
* Copyright (c) @__year__@ Franz Wilhelmstötter
*
* 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.
*
* Author:
* Franz Wilhelmstötter (franz.wilhelmstoetter@gmx.at)
*/
package org.jenetics;
import static java.lang.String.format;
import static java.util.Objects.requireNonNull;
import java.util.Random;
import org.jenetics.internal.util.HashBuilder;
import org.jenetics.util.Factory;
import org.jenetics.util.RandomRegistry;
/**
* In tournament selection the best individual from a random sample of <i>s</i>
* individuals is chosen from the population <i>P<sub>g</sub></i>. The samples
* are drawn with replacement. An individual will win a tournament only if its
* fitness is greater than the fitness of the other <i>s-1</i> competitors.
* Note that the worst individual never survives, and the best individual wins
* in all the tournaments it participates. The selection pressure can be varied
* by changing the tournament size <i>s</i> . For large values of <i>s</i>, weak
* individuals have less chance being selected.
*
* @see <a href="http://en.wikipedia.org/wiki/Tournament_selection">Tournament selection</a>
*
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @since 1.0
* @version 1.0 — <em>$Date: 2014-02-27 $</em>
*/
public class TournamentSelector<
G extends Gene<?, G>,
C extends Comparable<? super C>
>
implements Selector<G, C>
{
private final int _sampleSize;
/**
* Create a tournament selector with the give sample size. The sample size
* must be greater than one.
*
* @throws IllegalArgumentException if the sample size is smaller than two.
*/
public TournamentSelector(final int sampleSize) {
if (sampleSize < 2) {
throw new IllegalArgumentException(
"Sample size must be greater than one, but was " + sampleSize
);
}
_sampleSize = sampleSize;
}
/**
* Create a tournament selector with sample size two.
*/
public TournamentSelector() {
this(2);
}
/**
* @throws IllegalArgumentException if the sample size is greater than the
* population size or {@code count} is greater the the population
* size or the _sampleSize is greater the the population size.
* @throws NullPointerException if the {@code population} is {@code null}.
*/
@Override
public Population<G, C> select(
final Population<G, C> population,
final int count,
final Optimize opt
) {
requireNonNull(population, "Population");
requireNonNull(opt, "Optimization");
if (count < 0) {
throw new IllegalArgumentException(format(
"Selection count must be greater or equal then zero, but was %s",
count
));
}
if (count > population.size()) {
throw new IllegalArgumentException(format(
"Selection size greater than population size: %s > %s",
count, population.size()
));
}
if (_sampleSize > population.size()) {
throw new IllegalArgumentException(format(
"Tournament size is greater than the population size! %d > %d.",
_sampleSize, population.size()
));
}
final Population<G, C> pop = new Population<>(count);
final Factory<Phenotype<G, C>> factory = factory(
population, opt, _sampleSize, RandomRegistry.getRandom()
);
return pop.fill(factory, count);
}
private static <
G extends Gene<?, G>,
C extends Comparable<? super C>
>
Factory<Phenotype<G, C>> factory(
final Population<G, C> population,
final Optimize opt,
final int sampleSize,
final Random random
) {
return new Factory<Phenotype<G, C>>() {
@Override
public Phenotype<G, C> newInstance() {
return select(population, opt, sampleSize, random);
}
};
}
private static <
G extends Gene<?, G>,
C extends Comparable<? super C>
>
Phenotype<G, C> select(
final Population<G, C> population,
final Optimize opt,
final int sampleSize,
final Random random
) {
final int N = population.size();
Phenotype<G, C> winner = population.get(random.nextInt(N));
for (int j = 0; j < sampleSize; ++j) {
final Phenotype<G, C> selection = population.get(random.nextInt(N));
if (opt.compare(selection, winner) > 0) {
winner = selection;
}
}
assert (winner != null);
return winner;
}
@Override
public int hashCode() {
return HashBuilder.of(getClass()).and(_sampleSize).value();
}
@Override
public boolean equals(final Object obj) {
if (obj == this) {
return true;
}
if (obj == null || obj.getClass() != getClass()) {
return false;
}
final TournamentSelector<?, ?> selector = (TournamentSelector<?, ?>)obj;
return _sampleSize == selector._sampleSize;
}
/**
* @deprecated Will be removed.
*/
@Deprecated
public static <SG extends Gene<?, SG>, SC extends Comparable<SC>>
TournamentSelector<SG, SC> valueOf(final int sampleSize) {
return new TournamentSelector<>(sampleSize);
}
/**
* @deprecated Will be removed.
*/
@Deprecated
public static <SG extends Gene<?, SG>, SC extends Comparable<SC>>
TournamentSelector<SG, SC> valueOf() {
return new TournamentSelector<>();
}
@Override
public String toString() {
return format("%s[s=%d]", getClass().getSimpleName(), _sampleSize);
}
}