001 /*
002 * Java Genetic Algorithm Library (jenetics-3.0.0).
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.math.random.indexes;
024
025 import java.util.Random;
026
027 import org.jenetics.internal.math.base;
028 import org.jenetics.internal.util.Equality;
029 import org.jenetics.internal.util.Hash;
030
031 import org.jenetics.util.MSeq;
032 import org.jenetics.util.RandomRegistry;
033
034 /**
035 * The GaussianMutator class performs the mutation of a {@link NumericGene}.
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 3.0 — <em>$Date: 2014-12-28 $</em>
051 */
052 public final class GaussianMutator<
053 G extends NumericGene<?, G>,
054 C extends Comparable<? super C>
055 >
056 extends Mutator<G, C>
057 {
058
059 public GaussianMutator(final double probability) {
060 super(probability);
061 }
062
063 public GaussianMutator() {
064 this(DEFAULT_ALTER_PROBABILITY);
065 }
066
067 @Override
068 protected int mutate(final MSeq<G> genes, final double p) {
069 final Random random = RandomRegistry.getRandom();
070
071 return (int)indexes(random, genes.length(), p)
072 .peek(i -> genes.set(i, mutate(genes.get(i), random)))
073 .count();
074 }
075
076 G mutate(final G gene, final Random random) {
077 final double std =
078 (gene.getMax().doubleValue() - gene.getMin().doubleValue())*0.25;
079
080 return gene.newInstance(base.clamp(
081 random.nextGaussian()*std + gene.doubleValue(),
082 gene.getMin().doubleValue(),
083 gene.getMax().doubleValue()
084 ));
085 }
086
087 @Override
088 public int hashCode() {
089 return Hash.of(getClass()).and(super.hashCode()).value();
090 }
091
092 @Override
093 public boolean equals(final Object obj) {
094 return Equality.of(this, obj).test(super::equals);
095 }
096
097 @Override
098 public String toString() {
099 return format(
100 "%s[p=%f]",
101 getClass().getSimpleName(),
102 _probability
103 );
104 }
105
106 }
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