001/*
002 * Java Genetic Algorithm Library (jenetics-8.0.0).
003 * Copyright (c) 2007-2024 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 */
020package io.jenetics;
021
022import static java.lang.Math.clamp;
023import static java.lang.Math.nextDown;
024
025import java.util.random.RandomGenerator;
026
027/**
028 * The GaussianMutator class performs the mutation of a {@link NumericGene}.
029 * This mutator picks a new value based on a Gaussian distribution around the
030 * current value of the gene. The variance of the new value (before clipping to
031 * the allowed gene range) will be
032 * <p>
033 * <img
034 *     src="doc-files/gaussian-mutator-var.svg"
035 *     alt="\hat{\sigma }^2 = \left ( \frac{ g_{max} - g_{min} }{4}\right )^2"
036 * >
037 * </p>
038 * The new value will be cropped to the gene's boundaries.
039 *
040 *
041 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
042 * @since 1.0
043 * @version 6.1
044 */
045public class GaussianMutator<
046        G extends NumericGene<?, G>,
047        C extends Comparable<? super C>
048>
049        extends Mutator<G, C>
050{
051
052        public GaussianMutator(final double probability) {
053                super(probability);
054        }
055
056        public GaussianMutator() {
057                this(DEFAULT_ALTER_PROBABILITY);
058        }
059
060        @Override
061        protected G mutate(final G gene, final RandomGenerator random) {
062                return gene.isValid() ? mutate0(gene, random) : gene;
063        }
064
065        private G mutate0(final G gene, final RandomGenerator random) {
066                final double min = gene.min().doubleValue();
067                final double max = gene.max().doubleValue();
068                final double stddev = (max - min)*0.25;
069
070                final double value = gene.doubleValue();
071                final double gaussian = random.nextGaussian(value, stddev);
072                return gene.newInstance(clamp(gaussian, min, nextDown(max)));
073        }
074
075}