001 /*
002 * Java Genetic Algorithm Library (jenetics-6.1.0).
003 * Copyright (c) 2007-2020 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 ([email protected])
019 */
020 package io.jenetics;
021
022 import static java.lang.Math.min;
023
024 import java.util.Random;
025
026 import io.jenetics.util.MSeq;
027 import io.jenetics.util.RandomRegistry;
028
029 /**
030 * <p>
031 * Performs a <a href="http://en.wikipedia.org/wiki/Crossover_%28genetic_algorithm%29">
032 * Crossover</a> of two {@link Chromosome}. This crossover implementation can
033 * handle genotypes with different length (different number of chromosomes). It
034 * is guaranteed that chromosomes with the the same (genotype) index are chosen
035 * for <em>crossover</em>.
036 * </p>
037 * <p>
038 * The order ({@link #order()}) of this Recombination implementation is two.
039 * </p>
040 *
041 * @param <G> the gene type.
042 *
043 * @author <a href="mailto:[email protected]">Franz Wilhelmstötter</a>
044 * @since 1.0
045 * @version 4.0
046 */
047 public abstract class Crossover<
048 G extends Gene<?, G>,
049 C extends Comparable<? super C>
050 >
051 extends Recombinator<G, C>
052 {
053
054 /**
055 * Constructs an alterer with a given recombination probability.
056 *
057 * @param probability the recombination probability
058 * @throws IllegalArgumentException if the {@code probability} is not in the
059 * valid range of {@code [0, 1]}
060 */
061 protected Crossover(final double probability) {
062 super(probability, 2);
063 }
064
065 @Override
066 protected final int recombine(
067 final MSeq<Phenotype<G, C>> population,
068 final int[] individuals,
069 final long generation
070 ) {
071 assert individuals.length == 2 : "Required order of 2";
072 final Random random = RandomRegistry.random();
073
074 final var pt1 = population.get(individuals[0]);
075 final var pt2 = population.get(individuals[1]);
076 final var gt1 = pt1.genotype();
077 final var gt2 = pt2.genotype();
078
079 //Choosing the Chromosome index for crossover.
080 final int chIndex = random.nextInt(min(gt1.length(), gt2.length()));
081
082 final var c1 = MSeq.of(gt1);
083 final var c2 = MSeq.of(gt2);
084 final var genes1 = MSeq.of(c1.get(chIndex));
085 final var genes2 = MSeq.of(c2.get(chIndex));
086
087 crossover(genes1, genes2);
088
089 c1.set(chIndex, c1.get(chIndex).newInstance(genes1.toISeq()));
090 c2.set(chIndex, c2.get(chIndex).newInstance(genes2.toISeq()));
091
092 //Creating two new Phenotypes and exchanging it with the old.
093 population.set(
094 individuals[0],
095 Phenotype.of(Genotype.of(c1), generation)
096 );
097 population.set(
098 individuals[1],
099 Phenotype.of(Genotype.of(c2), generation)
100 );
101
102 return order();
103 }
104
105 /**
106 * Template method which performs the crossover. The arguments given are
107 * mutable non null arrays of the same length.
108 *
109 * @param that the genes of the first chromosome
110 * @param other the genes of the other chromosome
111 * @return the number of altered genes
112 */
113 protected abstract int crossover(final MSeq<G> that, final MSeq<G> other);
114
115 }
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