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 java.lang.Math.pow;
023 import static java.lang.String.format;
024
025 import java.util.Objects;
026
027 import io.jenetics.internal.util.Hash;
028 import io.jenetics.util.Seq;
029
030 /**
031 * <p>
032 * An alternative to the "weak" {@code LinearRankSelector} is to assign
033 * survival probabilities to the sorted individuals using an exponential
034 * function.
035 * </p>
036 * <p><img
037 * src="doc-files/exponential-rank-selector.gif"
038 * alt="P(i)=\left(c-1\right)\frac{c^{i-1}}{c^{N}-1}"
039 * >,
040 * </p>
041 * where <i>c</i> must within the range {@code [0..1)}.
042 *
043 * <p>
044 * A small value of <i>c</i> increases the probability of the best phenotypes to
045 * be selected. If <i>c</i> is set to zero, the selection probability of the best
046 * phenotype is set to one. The selection probability of all other phenotypes is
047 * zero. A value near one equalizes the selection probabilities.
048 * </p>
049 * <p>
050 * This selector sorts the population in descending order while calculating the
051 * selection probabilities.
052 * </p>
053 *
054 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
055 * @since 1.0
056 * @version 4.0
057 */
058 public final class ExponentialRankSelector<
059 G extends Gene<?, G>,
060 C extends Comparable<? super C>
061 >
062 extends ProbabilitySelector<G, C>
063 {
064
065 private final double _c;
066
067 /**
068 * Create a new exponential rank selector.
069 *
070 * @param c the <i>c</i> value.
071 * @throws IllegalArgumentException if {@code c} is not within the range
072 * {@code [0..1)}.
073 */
074 public ExponentialRankSelector(final double c) {
075 super(true);
076
077 if (c < 0.0 || c >= 1.0) {
078 throw new IllegalArgumentException(format(
079 "Value %s is out of range [0..1): ", c
080 ));
081 }
082 _c = c;
083 }
084
085 /**
086 * Create a new selector with default value of 0.975.
087 */
088 public ExponentialRankSelector() {
089 this(0.975);
090 }
091
092 /**
093 * This method sorts the population in descending order while calculating the
094 * selection probabilities.
095 */
096 @Override
097 protected double[] probabilities(
098 final Seq<Phenotype<G, C>> population,
099 final int count
100 ) {
101 assert population != null : "Population must not be null. ";
102 assert !population.isEmpty() : "Population is empty.";
103 assert count > 0 : "Population to select must be greater than zero. ";
104
105 final double N = population.size();
106 final double[] probabilities = new double[population.size()];
107
108 final double b = (_c - 1.0)/(pow(_c, N) - 1.0);
109 for (int i = 0; i < probabilities.length; ++i) {
110 probabilities[i] = pow(_c, i)*b;
111 }
112
113 return probabilities;
114 }
115
116 @Override
117 public int hashCode() {
118 return Hash.of(getClass()).and(_c).value();
119 }
120
121 @Override
122 public boolean equals(final Object obj) {
123 return obj == this ||
124 obj instanceof ExponentialRankSelector &&
125 Objects.equals(((ExponentialRankSelector) obj)._c, _c);
126 }
127
128 @Override
129 public String toString() {
130 return format("%s[c=%f]", getClass().getSimpleName(), _c);
131 }
132
133 }
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