001 /*
002 * Licensed to the Apache Software Foundation (ASF) under one or more
003 * contributor license agreements. See the NOTICE file distributed with
004 * this work for additional information regarding copyright ownership.
005 * The ASF licenses this file to You under the Apache License, Version 2.0
006 * (the "License"); you may not use this file except in compliance with
007 * the License. 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 package org.apache.commons.math3.genetics;
018
019 import java.util.ArrayList;
020 import java.util.List;
021
022 import org.apache.commons.math3.exception.DimensionMismatchException;
023 import org.apache.commons.math3.exception.MathIllegalArgumentException;
024 import org.apache.commons.math3.exception.OutOfRangeException;
025 import org.apache.commons.math3.exception.util.LocalizedFormats;
026 import org.apache.commons.math3.random.RandomGenerator;
027
028 /**
029 * Perform Uniform Crossover [UX] on the specified chromosomes. A fixed mixing
030 * ratio is used to combine genes from the first and second parents, e.g. using a
031 * ratio of 0.5 would result in approximately 50% of genes coming from each
032 * parent. This is typically a poor method of crossover, but empirical evidence
033 * suggests that it is more exploratory and results in a larger part of the
034 * problem space being searched.
035 * <p>
036 * This crossover policy evaluates each gene of the parent chromosomes by chosing a
037 * uniform random number {@code p} in the range [0, 1]. If {@code p} < {@code ratio},
038 * the parent genes are swapped. This means with a ratio of 0.7, 30% of the genes from the
039 * first parent and 70% from the second parent will be selected for the first offspring (and
040 * vice versa for the second offspring).
041 * <p>
042 * This policy works only on {@link AbstractListChromosome}, and therefore it
043 * is parameterized by T. Moreover, the chromosomes must have same lengths.
044 *
045 * @see <a href="http://en.wikipedia.org/wiki/Crossover_%28genetic_algorithm%29">Crossover techniques (Wikipedia)</a>
046 * @see <a href="http://www.obitko.com/tutorials/genetic-algorithms/crossover-mutation.php">Crossover (Obitko.com)</a>
047 * @see <a href="http://www.tomaszgwiazda.com/uniformX.htm">Uniform crossover</a>
048 * @param <T> generic type of the {@link AbstractListChromosome}s for crossover
049 * @since 3.1
050 * @version $Id: UniformCrossover.java 1385297 2012-09-16 16:05:57Z tn $
051 */
052 public class UniformCrossover<T> implements CrossoverPolicy {
053
054 /** The mixing ratio. */
055 private final double ratio;
056
057 /**
058 * Creates a new {@link UniformCrossover} policy using the given mixing ratio.
059 *
060 * @param ratio the mixing ratio
061 * @throws OutOfRangeException if the mixing ratio is outside the [0, 1] range
062 */
063 public UniformCrossover(final double ratio) throws OutOfRangeException {
064 if (ratio < 0.0d || ratio > 1.0d) {
065 throw new OutOfRangeException(LocalizedFormats.CROSSOVER_RATE, ratio, 0.0d, 1.0d);
066 }
067 this.ratio = ratio;
068 }
069
070 /**
071 * Returns the mixing ratio used by this {@link CrossoverPolicy}.
072 *
073 * @return the mixing ratio
074 */
075 public double getRatio() {
076 return ratio;
077 }
078
079 /**
080 * {@inheritDoc}
081 *
082 * @throws MathIllegalArgumentException iff one of the chromosomes is
083 * not an instance of {@link AbstractListChromosome}
084 * @throws DimensionMismatchException if the length of the two chromosomes is different
085 */
086 @SuppressWarnings("unchecked")
087 public ChromosomePair crossover(final Chromosome first, final Chromosome second)
088 throws DimensionMismatchException, MathIllegalArgumentException {
089
090 if (!(first instanceof AbstractListChromosome<?> && second instanceof AbstractListChromosome<?>)) {
091 throw new MathIllegalArgumentException(LocalizedFormats.INVALID_FIXED_LENGTH_CHROMOSOME);
092 }
093 return mate((AbstractListChromosome<T>) first, (AbstractListChromosome<T>) second);
094 }
095
096 /**
097 * Helper for {@link #crossover(Chromosome, Chromosome)}. Performs the actual crossover.
098 *
099 * @param first the first chromosome
100 * @param second the second chromosome
101 * @return the pair of new chromosomes that resulted from the crossover
102 * @throws DimensionMismatchException if the length of the two chromosomes is different
103 */
104 private ChromosomePair mate(final AbstractListChromosome<T> first,
105 final AbstractListChromosome<T> second) throws DimensionMismatchException {
106 final int length = first.getLength();
107 if (length != second.getLength()) {
108 throw new DimensionMismatchException(second.getLength(), length);
109 }
110
111 // array representations of the parents
112 final List<T> parent1Rep = first.getRepresentation();
113 final List<T> parent2Rep = second.getRepresentation();
114 // and of the children
115 final List<T> child1Rep = new ArrayList<T>(first.getLength());
116 final List<T> child2Rep = new ArrayList<T>(second.getLength());
117
118 final RandomGenerator random = GeneticAlgorithm.getRandomGenerator();
119
120 for (int index = 0; index < length; index++) {
121
122 if (random.nextDouble() < ratio) {
123 // swap the bits -> take other parent
124 child1Rep.add(parent2Rep.get(index));
125 child2Rep.add(parent1Rep.get(index));
126 } else {
127 child1Rep.add(parent1Rep.get(index));
128 child2Rep.add(parent2Rep.get(index));
129 }
130 }
131
132 return new ChromosomePair(first.newFixedLengthChromosome(child1Rep),
133 second.newFixedLengthChromosome(child2Rep));
134 }
135 }