MonteCarloSelector.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 javolution.lang.Immutable;
import org.jenetics.internal.util.HashBuilder;
import org.jenetics.util.RandomRegistry;
/**
* The Monte Carlo selector selects the individuals from a given population
* randomly. This selector can be used to measure the performance of a other
* selectors. In general, the performance of a selector should be better than
* the selection performance of the Monte Carlo selector.
*
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @since 1.0
* @version 1.0 — <em>$Date: 2014-02-27 $</em>
*/
public final class MonteCarloSelector<
G extends Gene<?, G>,
C extends Comparable<? super C>
>
implements
Selector<G, C>,
Immutable
{
public MonteCarloSelector() {
}
@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 %d.",
count
));
}
final Population<G, C> selection = new Population<>(count);
if (count > 0) {
final Random random = RandomRegistry.getRandom();
final int size = population.size();
for (int i = 0; i < count; ++i) {
final int pos = random.nextInt(size);
selection.add(population.get(pos));
}
}
return selection;
}
@Override
public int hashCode() {
return HashBuilder.of(getClass()).value();
}
@Override
public boolean equals(final Object obj) {
return obj == this || obj instanceof MonteCarloSelector<?, ?>;
}
@Override
public String toString() {
return format("%s", getClass().getSimpleName());
}
}