001 /*
002 * Java Genetic Algorithm Library (jenetics-4.1.0).
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.stat.DoubleSummary.min;
023
024 import java.util.Arrays;
025
026 import io.jenetics.internal.math.DoubleAdder;
027 import io.jenetics.internal.util.Equality;
028 import io.jenetics.internal.util.Hash;
029 import io.jenetics.util.Seq;
030
031 /**
032 * The roulette-wheel selector is also known as fitness proportional selector,
033 * but in the <em>Jenetics</em> library it is implemented as probability selector.
034 * The fitness value <i>f<sub>i</sub></i> is used to calculate the selection
035 * probability of individual <i>i</i>.
036 *
037 * @see <a href="http://en.wikipedia.org/wiki/Roulette_wheel_selection">
038 * Wikipedia: Roulette wheel selection
039 * </a>
040 *
041 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
042 * @since 1.0
043 * @version 4.0
044 */
045 public class RouletteWheelSelector<
046 G extends Gene<?, G>,
047 N extends Number & Comparable<? super N>
048 >
049 extends ProbabilitySelector<G, N>
050 {
051
052 public RouletteWheelSelector() {
053 this(false);
054 }
055
056 protected RouletteWheelSelector(final boolean sorted) {
057 super(sorted);
058 }
059
060 @Override
061 protected double[] probabilities(
062 final Seq<Phenotype<G, N>> population,
063 final int count
064 ) {
065 assert population != null : "Population must not be null. ";
066 assert population.nonEmpty() : "Population is empty.";
067 assert count > 0 : "Population to select must be greater than zero. ";
068
069 // Copy the fitness values to probabilities arrays.
070 final double[] fitness = new double[population.size()];
071 for (int i = population.size(); --i >= 0;) {
072 fitness[i] = population.get(i).getFitness().doubleValue();
073 }
074
075 final double worst = Math.min(min(fitness), 0.0);
076 final double sum = DoubleAdder.sum(fitness) - worst*population.size();
077
078 if (eq(sum, 0.0)) {
079 Arrays.fill(fitness, 1.0/population.size());
080 } else {
081 for (int i = population.size(); --i >= 0;) {
082 fitness[i] = (fitness[i] - worst)/sum;
083 }
084 }
085
086 return fitness;
087 }
088
089 @Override
090 public int hashCode() {
091 return Hash.of(getClass()).value();
092 }
093
094 @Override
095 public boolean equals(final Object obj) {
096 return Equality.ofType(this, obj);
097 }
098
099 @Override
100 public String toString() {
101 return getClass().getSimpleName();
102 }
103
104 }
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