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 static java.util.Objects.requireNonNull;
023
024 import io.jenetics.util.ISeq;
025 import io.jenetics.util.MSeq;
026 import io.jenetics.util.RandomRegistry;
027 import io.jenetics.util.Seq;
028
029 /**
030 * {@code StochasticUniversalSelector} is a method for selecting a
031 * population according to some given probability in a way that minimizes chance
032 * fluctuations. It can be viewed as a type of roulette game where now we have
033 * P equally spaced points which we spin.
034 *
035 * <p>
036 * <img src="doc-files/StochasticUniversalSelection.svg" width="400"
037 * alt="Selector">
038 * </p>
039 *
040 * The figure above shows how the stochastic-universal selection works; <i>n</i>
041 * is the number of individuals to select.
042 *
043 * @see <a href="https://secure.wikimedia.org/wikipedia/en/wiki/Stochastic_universal_sampling">
044 * Wikipedia: Stochastic universal sampling
045 * </a>
046 *
047 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
048 * @since 1.0
049 * @version 5.0
050 */
051 public class StochasticUniversalSelector<
052 G extends Gene<?, G>,
053 N extends Number & Comparable<? super N>
054 >
055 extends RouletteWheelSelector<G, N>
056 {
057
058 public StochasticUniversalSelector() {
059 super(true);
060 }
061
062 /**
063 * This method sorts the population in descending order while calculating the
064 * selection probabilities.
065 */
066 @Override
067 public ISeq<Phenotype<G, N>> select(
068 final Seq<Phenotype<G, N>> population,
069 final int count,
070 final Optimize opt
071 ) {
072 requireNonNull(population, "Population");
073 if (count < 0) {
074 throw new IllegalArgumentException(
075 "Selection count must be greater or equal then zero, but was " +
076 count
077 );
078 }
079
080 if (count == 0 || population.isEmpty()) {
081 return ISeq.empty();
082 }
083
084 final MSeq<Phenotype<G, N>> selection = MSeq.ofLength(count);
085
086 final Seq<Phenotype<G, N>> pop = _sorted
087 ? population.asISeq().copy().sort(POPULATION_COMPARATOR)
088 : population;
089
090 final double[] probabilities = probabilities(pop, count, opt);
091 assert pop.size() == probabilities.length;
092
093 //Calculating the equal spaces random points.
094 final double delta = 1.0/count;
095 final double[] points = new double[count];
096 points[0] = RandomRegistry.random().nextDouble()*delta;
097 for (int i = 1; i < count; ++i) {
098 points[i] = delta*i;
099 }
100
101 int j = 0;
102 double prop = 0;
103 for (int i = 0; i < count; ++i) {
104 while (points[i] > prop) {
105 prop += probabilities[j];
106 ++j;
107 }
108
109 selection.set(i, pop.get(j%pop.size()));
110 }
111
112 return selection.toISeq();
113 }
114
115 }
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