001 /*
002 * Java Genetic Algorithm Library (jenetics-1.5.0).
003 * Copyright (c) 2007-2013 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.util.object.hashCodeOf;
024
025 import java.util.Random;
026
027 import javolution.lang.Immutable;
028
029 import org.jenetics.util.IndexStream;
030 import org.jenetics.util.MSeq;
031 import org.jenetics.util.RandomRegistry;
032 import org.jenetics.util.math;
033
034 /**
035 * The GaussianMutator class performs the mutation of a {@link NumberGene}.
036 * This mutator picks a new value based on a Gaussian distribution around the
037 * current value of the gene. The variance of the new value (before clipping to
038 * the allowed gene range) will be
039 * <p>
040 * <img
041 * src="doc-files/gaussian-mutator-var.gif"
042 * alt="\hat{\sigma }^2 = \left ( \frac{ g_{max} - g_{min} }{4}\right )^2"
043 * >
044 * </p>
045 * The new value will be cropped to the gene's boundaries.
046 *
047 *
048 * @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
049 * @since 1.0
050 * @version 1.0 — <em>$Date: 2013-11-28 $</em>
051 */
052 public final class GaussianMutator<G extends NumberGene<?, G>>
053 extends Mutator<G>
054 implements Immutable
055 {
056
057 public GaussianMutator() {
058 }
059
060 public GaussianMutator(final double probability) {
061 super(probability);
062 }
063
064 @Override
065 protected int mutate(final MSeq<G> genes, final double p) {
066 final Random random = RandomRegistry.getRandom();
067 final IndexStream stream = IndexStream.Random(genes.length(), p);
068
069 int alterations = 0;
070 for (int i = stream.next(); i != -1; i = stream.next()) {
071 genes.set(i, mutate(genes.get(i), random));
072
073 ++alterations;
074 }
075
076 return alterations;
077 }
078
079 G mutate(final G gene, final Random random) {
080 final double std = (
081 gene.getMax().doubleValue() - gene.getMin().doubleValue()
082 )*0.25;
083
084 return gene.newInstance(math.clamp(
085 random.nextGaussian()*std + gene.doubleValue(),
086 gene.getMin().doubleValue(),
087 gene.getMax().doubleValue()
088 ));
089 }
090
091 @Override
092 public int hashCode() {
093 return hashCodeOf(getClass()).and(super.hashCode()).value();
094 }
095
096 @Override
097 public boolean equals(final Object obj) {
098 if (obj == this) {
099 return true;
100 }
101 if (obj == null || obj.getClass() != getClass()) {
102 return false;
103 }
104
105 return super.equals(obj);
106 }
107
108 @Override
109 public String toString() {
110 return format(
111 "%s[p=%f]",
112 getClass().getSimpleName(),
113 _probability
114 );
115 }
116
117 }
118
119
120
121
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