RouletteWheelSelector.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.Math.abs;
import static org.jenetics.util.math.pow;
import static org.jenetics.util.math.statistics.min;
import static org.jenetics.util.math.statistics.sum;
import static org.jenetics.util.math.ulpDistance;
import java.util.Arrays;
import javolution.lang.Immutable;
import org.jenetics.internal.util.HashBuilder;
/**
* The roulette-wheel selector is also known as fitness proportional selector,
* but in the <em>Jenetics</em> library it is implemented as probability selector.
* The fitness value <i>f<sub>i</sub></i> is used to calculate the selection
* probability of individual <i>i</i>.
*
* @see <a href="http://en.wikipedia.org/wiki/Roulette_wheel_selection">
* Wikipedia: Roulette wheel 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 RouletteWheelSelector<
G extends Gene<?, G>,
N extends Number & Comparable<? super N>
>
extends ProbabilitySelector<G, N>
implements Immutable
{
private static final long MAX_ULP_DISTANCE = pow(10, 9);
public RouletteWheelSelector() {
}
@Override
protected double[] probabilities(
final Population<G, N> population,
final int count
) {
assert(population != null) : "Population can not be null. ";
assert(count > 0) : "Population to select must be greater than zero. ";
// Copy the fitness values to probabilities arrays.
final double[] probabilities = new double[population.size()];
for (int i = population.size(); --i >= 0;) {
probabilities[i] = population.get(i).getFitness().doubleValue();
}
final double worst = Math.min(min(probabilities), 0.0);
final double sum = sum(probabilities) - worst*population.size();
if (abs(ulpDistance(sum, 0.0)) > MAX_ULP_DISTANCE) {
for (int i = population.size(); --i >= 0;) {
probabilities[i] = (probabilities[i] - worst)/sum;
}
} else {
Arrays.fill(probabilities, 1.0/population.size());
}
assert (sum2one(probabilities)) : "Probabilities doesn't sum to one.";
return probabilities;
}
@Override
public int hashCode() {
return HashBuilder.of(getClass()).value();
}
@Override
public boolean equals(final Object obj) {
return obj == this || obj != null && obj.getClass() == getClass();
}
@Override
public String toString() {
return getClass().getSimpleName();
}
}