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.lang.String.format;
023 import static org.jenetics.internal.math.base.clamp;
024 import static org.jenetics.internal.math.random.indexes;
025
026 import java.util.Random;
027
028 import org.jenetics.internal.util.Hash;
029
030 import org.jenetics.util.MSeq;
031 import org.jenetics.util.RandomRegistry;
032
033 /**
034 * The GaussianMutator class performs the mutation of a {@link NumericGene}.
035 * This mutator picks a new value based on a Gaussian distribution around the
036 * current value of the gene. The variance of the new value (before clipping to
037 * the allowed gene range) will be
038 * <p>
039 * <img
040 * src="doc-files/gaussian-mutator-var.gif"
041 * alt="\hat{\sigma }^2 = \left ( \frac{ g_{max} - g_{min} }{4}\right )^2"
042 * >
043 * </p>
044 * The new value will be cropped to the gene's boundaries.
045 *
046 *
047 * @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
048 * @since 1.0
049 * @version 3.0
050 */
051 public final class GaussianMutator<
052 G extends NumericGene<?, G>,
053 C extends Comparable<? super C>
054 >
055 extends Mutator<G, C>
056 {
057
058 public GaussianMutator(final double probability) {
059 super(probability);
060 }
061
062 public GaussianMutator() {
063 this(DEFAULT_ALTER_PROBABILITY);
064 }
065
066 @Override
067 protected int mutate(final MSeq<G> genes, final double p) {
068 final Random random = RandomRegistry.getRandom();
069
070 return (int)indexes(random, genes.length(), p)
071 .peek(i -> genes.set(i, mutate(genes.get(i), random)))
072 .count();
073 }
074
075 G mutate(final G gene, final Random random) {
076 final double min = gene.getMin().doubleValue();
077 final double max = gene.getMax().doubleValue();
078 final double std = (max - min)*0.25;
079
080 final double value = gene.doubleValue();
081 final double gaussian = random.nextGaussian();
082 return gene.newInstance(clamp(gaussian*std + value, min, max));
083 }
084
085 @Override
086 public int hashCode() {
087 return Hash.of(getClass()).and(super.hashCode()).value();
088 }
089
090 @Override
091 public boolean equals(final Object obj) {
092 return obj instanceof GaussianMutator && super.equals(obj);
093 }
094
095 @Override
096 public String toString() {
097 return format(
098 "%s[p=%f]",
099 getClass().getSimpleName(),
100 _probability
101 );
102 }
103
104 }
|