001 /*
002 * Java Genetic Algorithm Library (jenetics-3.9.0).
003 * Copyright (c) 2007-2017 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@gmx.at)
019 */
020 package org.jenetics;
021
022 import static java.util.Objects.requireNonNull;
023
024 import org.jenetics.internal.util.Equality;
025 import org.jenetics.internal.util.Hash;
026
027 import org.jenetics.util.RandomRegistry;
028
029 /**
030 * {@code StochasticUniversalSelector} is a method for selecting a
031 * population according to some given probability in a way that minimize 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@gmx.at">Franz Wilhelmstötter</a>
048 * @since 1.0
049 * @version 3.2
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. (The method {@link Population#populationSort()} is called
065 * by this method.)
066 */
067 @Override
068 public Population<G, N> select(
069 final Population<G, N> population,
070 final int count,
071 final Optimize opt
072 ) {
073 requireNonNull(population, "Population");
074 if (count < 0) {
075 throw new IllegalArgumentException(
076 "Selection count must be greater or equal then zero, but was " +
077 count
078 );
079 }
080
081 final Population<G, N> selection = new Population<>(count);
082 if (count == 0 || population.isEmpty()) {
083 return selection;
084 }
085
086 final Population<G, N> pop = copy(population);
087 final double[] probabilities = probabilities(pop, count, opt);
088 assert pop.size() == probabilities.length;
089
090 //Calculating the equally spaces random points.
091 final double delta = 1.0/count;
092 final double[] points = new double[count];
093 points[0] = RandomRegistry.getRandom().nextDouble()*delta;
094 for (int i = 1; i < count; ++i) {
095 points[i] = delta*i;
096 }
097
098 int j = 0;
099 double prop = 0;
100 for (int i = 0; i < count; ++i) {
101 while (points[i] > prop) {
102 prop += probabilities[j];
103 ++j;
104 }
105 selection.add(pop.get(j%pop.size()));
106 }
107
108 return selection;
109 }
110
111 @Override
112 public int hashCode() {
113 return Hash.of(getClass()).and(super.hashCode()).value();
114 }
115
116 @Override
117 public boolean equals(final Object obj) {
118 return Equality.of(this, obj).test(super::equals);
119 }
120
121 @Override
122 public String toString() {
123 return getClass().getSimpleName();
124 }
125
126 }
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