001 /*
002 * Java Genetic Algorithm Library (jenetics-1.6.0).
003 * Copyright (c) 2007-2014 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.lang.String.format;
023 import static org.jenetics.internal.util.object.eq;
024
025 import javolution.lang.Immutable;
026
027 import org.jenetics.internal.util.HashBuilder;
028
029
030 /**
031 * <p>
032 * In linear-ranking selection the individuals are sorted according to their
033 * fitness values. The rank <i>N</i> is assignee to the best individual and the
034 * rank 1 to the worst individual. The selection probability <i>P(i)</i> of
035 * individual <i>i</i> is linearly assigned to the individuals according to
036 * their rank.
037 * </p>
038 * <p/><img
039 * src="doc-files/linear-rank-selector.gif"
040 * alt="P(i)=\frac{1}{N}\left(n^{-}+\left(n^{+}-n^{-}\right)\frac{i-1}{N-1}\right)"
041 * >
042 * </p>
043 *
044 * Here <i>n</i><sup><i>-</i></sup>/<i>N</i> is the probability of the worst
045 * individual to be selected and <i>n</i><sup><i>+</i></sup>/<i>N</i> the
046 * probability of the best individual to be selected. As the population size is
047 * held constant, the conditions <i>n</i><sup><i>+</i></sup> = 2 - <i>n</i><sup><i>-</i></sup>
048 * and <i>n</i><sup><i>-</i></sup> >= 0 must be fulfilled. Note that all individuals
049 * get a different rank, i.e., a different selection probability, even if the
050 * have the same fitness value. <p/>
051 *
052 * <i>
053 * T. Blickle, L. Thiele, A comparison of selection schemes used
054 * in evolutionary algorithms, Technical Report, ETH Zurich, 1997, page 37.
055 * <a href="http://citeseer.ist.psu.edu/blickle97comparison.html">
056 * http://citeseer.ist.psu.edu/blickle97comparison.html
057 * </a>
058 * </i>
059 *
060 * @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
061 * @since 1.0
062 * @version 1.0 — <em>$Date: 2014-03-01 $</em>
063 */
064 public final class LinearRankSelector<
065 G extends Gene<?, G>,
066 C extends Comparable<? super C>
067 >
068 extends ProbabilitySelector<G, C>
069 implements Immutable
070 {
071 private final double _nminus;
072 private final double _nplus;
073
074 /**
075 * Create a new LinearRankSelector with {@code nminus := 0.5}.
076 */
077 public LinearRankSelector() {
078 this(0.5);
079 }
080
081 /**
082 * Create a new LinearRankSelector with the given values for {@code nminus}.
083 *
084 * @param nminus {@code nminus/N} is the probability of the worst phenotype
085 * to be selected.
086 * @throws IllegalArgumentException if {@code nminus < 0}.
087 */
088 public LinearRankSelector(final double nminus) {
089 if (nminus < 0) {
090 throw new IllegalArgumentException(format(
091 "nminus is smaller than zero: %s", nminus
092 ));
093 }
094
095 _nminus = nminus;
096 _nplus = 2 - _nminus;
097 }
098
099 /**
100 * This method sorts the population in descending order while calculating the
101 * selection probabilities. (The method {@link Population#sort()} is called
102 * by this method.)
103 */
104 @Override
105 protected double[] probabilities(
106 final Population<G, C> population,
107 final int count
108 ) {
109 assert(population != null) : "Population can not be null. ";
110 assert(count > 0) : "Population to select must be greater than zero. ";
111
112 //Sort the population.
113 population.sort();
114
115 final double N = population.size();
116 final double[] probabilities = new double[population.size()];
117
118 for (int i = probabilities.length; --i >= 0;) {
119 probabilities[probabilities.length - i - 1] =
120 (_nminus + ((_nplus - _nminus)*i)/(N - 1))/N;
121 }
122
123 assert (sum2one(probabilities)) : "Probabilities doesn't sum to one.";
124 return probabilities;
125 }
126
127 @Override
128 public int hashCode() {
129 return HashBuilder.of(getClass()).and(_nminus).and(_nplus).value();
130 }
131
132 @Override
133 public boolean equals(final Object obj) {
134 if (obj == this) {
135 return true;
136 }
137 if (!(obj instanceof LinearRankSelector<?, ?>)) {
138 return false;
139 }
140
141 final LinearRankSelector<?, ?> selector = (LinearRankSelector<?, ?>)obj;
142 return eq(_nminus, selector._nminus) && eq(_nplus, selector._nplus);
143 }
144
145 @Override
146 public String toString() {
147 return format(
148 "%s[n-=%f, n+=%f]",
149 getClass().getSimpleName(), _nminus, _nplus
150 );
151 }
152
153 }
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