001 /*
002 * Java Genetic Algorithm Library (jenetics-4.3.0).
003 * Copyright (c) 2007-2018 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 static io.jenetics.internal.util.Hashes.hash;
023 import static io.jenetics.stat.DoubleSummary.min;
024
025 import java.util.Arrays;
026
027 import io.jenetics.internal.math.DoubleAdder;
028 import io.jenetics.util.Seq;
029
030 /**
031 * The roulette-wheel selector is also known as fitness proportional selector,
032 * but in the <em>Jenetics</em> library it is implemented as probability selector.
033 * The fitness value <i>f<sub>i</sub></i> is used to calculate the selection
034 * probability of individual <i>i</i>.
035 *
036 * @see <a href="http://en.wikipedia.org/wiki/Roulette_wheel_selection">
037 * Wikipedia: Roulette wheel selection
038 * </a>
039 *
040 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
041 * @since 1.0
042 * @version 4.0
043 */
044 public class RouletteWheelSelector<
045 G extends Gene<?, G>,
046 N extends Number & Comparable<? super N>
047 >
048 extends ProbabilitySelector<G, N>
049 {
050
051 public RouletteWheelSelector() {
052 this(false);
053 }
054
055 protected RouletteWheelSelector(final boolean sorted) {
056 super(sorted);
057 }
058
059 @Override
060 protected double[] probabilities(
061 final Seq<Phenotype<G, N>> population,
062 final int count
063 ) {
064 assert population != null : "Population must not be null. ";
065 assert population.nonEmpty() : "Population is empty.";
066 assert count > 0 : "Population to select must be greater than zero. ";
067
068 // Copy the fitness values to probabilities arrays.
069 final double[] fitness = new double[population.size()];
070 for (int i = population.size(); --i >= 0;) {
071 fitness[i] = population.get(i).getFitness().doubleValue();
072 }
073
074 final double worst = Math.min(min(fitness), 0.0);
075 final double sum = DoubleAdder.sum(fitness) - worst*population.size();
076
077 if (eq(sum, 0.0)) {
078 Arrays.fill(fitness, 1.0/population.size());
079 } else {
080 for (int i = population.size(); --i >= 0;) {
081 fitness[i] = (fitness[i] - worst)/sum;
082 }
083 }
084
085 return fitness;
086 }
087
088 @Override
089 public int hashCode() {
090 return hash(getClass());
091 }
092
093 @Override
094 public boolean equals(final Object obj) {
095 return obj == this || obj != null && getClass() == obj.getClass();
096 }
097
098 @Override
099 public String toString() {
100 return getClass().getSimpleName();
101 }
102
103 }
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