001 /*
002 * Java Genetic Algorithm Library (jenetics-8.0.0).
003 * Copyright (c) 2007-2024 Franz Wilhelmstötter
004 *
005 * Licensed under the Apache License, Version 2.0 (the "License");
006 * you may not use this file except in compliance with the License.
007 * You may obtain a copy of the License at
008 *
009 * http://www.apache.org/licenses/LICENSE-2.0
010 *
011 * Unless required by applicable law or agreed to in writing, software
012 * distributed under the License is distributed on an "AS IS" BASIS,
013 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
014 * See the License for the specific language governing permissions and
015 * limitations under the License.
016 *
017 * Author:
018 * Franz Wilhelmstötter (franz.wilhelmstoetter@gmail.com)
019 */
020 package io.jenetics;
021
022 import java.util.Arrays;
023
024 import io.jenetics.internal.math.DoubleAdder;
025 import io.jenetics.stat.DoubleSummary;
026 import io.jenetics.util.BaseSeq;
027 import io.jenetics.util.Seq;
028
029 /**
030 * The roulette-wheel selector is also known as fitness proportional selector,
031 * but in the <em>Jenetics</em> library it is implemented as probability selector.
032 * The fitness value <i>f<sub>i</sub></i> is used to calculate the selection
033 * probability of individual <i>i</i>.
034 *
035 * @see <a href="http://en.wikipedia.org/wiki/Roulette_wheel_selection">
036 * Wikipedia: Roulette wheel selection
037 * </a>
038 *
039 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
040 * @since 1.0
041 * @version 5.0
042 */
043 public class RouletteWheelSelector<
044 G extends Gene<?, G>,
045 N extends Number & Comparable<? super N>
046 >
047 extends ProbabilitySelector<G, N>
048 {
049
050 public RouletteWheelSelector() {
051 this(false);
052 }
053
054 protected RouletteWheelSelector(final boolean sorted) {
055 super(sorted);
056 }
057
058 @Override
059 protected double[] probabilities(
060 final Seq<Phenotype<G, N>> population,
061 final int count
062 ) {
063 assert population != null : "Population must not be null. ";
064 assert population.nonEmpty() : "Population is empty.";
065 assert count > 0 : "Population to select must be greater than zero. ";
066
067 final double[] fitness = fitnessOf(population);
068 sub(fitness, Math.min(DoubleSummary.min(fitness), 0.0));
069 final double sum = DoubleAdder.sum(fitness);
070
071 if (eq(sum, 0.0)) {
072 Arrays.fill(fitness, 1.0/population.size());
073 } else {
074 for (int i = fitness.length; --i >= 0;) {
075 fitness[i] = fitness[i]/sum;
076 }
077 }
078
079 return fitness;
080 }
081
082 private double[] fitnessOf(final BaseSeq<Phenotype<G, N>> population) {
083 final double[] fitness = new double[population.length()];
084 for (int i = fitness.length; --i >= 0;) {
085 final double fit = population.get(i).fitness().doubleValue();
086 fitness[i] = Double.isFinite(fit) ? fit : 0.0;
087 }
088 return fitness;
089 }
090
091 private static void sub(final double[] values, final double subtrahend) {
092 if (Double.compare(subtrahend, 0.0) != 0) {
093 for (int i = values.length; --i >= 0;) {
094 values[i] -= subtrahend;
095 }
096 }
097 }
098
099 @Override
100 public String toString() {
101 return getClass().getSimpleName();
102 }
103
104 }
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