001 /*
002 * Java Genetic Algorithm Library (jenetics-4.4.0).
003 * Copyright (c) 2007-2019 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 */
020 package io.jenetics;
021
022 import static java.lang.Math.min;
023 import static java.lang.String.format;
024
025 import java.util.Random;
026
027 import io.jenetics.util.ISeq;
028 import io.jenetics.util.MSeq;
029 import io.jenetics.util.Mean;
030 import io.jenetics.util.RandomRegistry;
031 import io.jenetics.util.Seq;
032
033 /**
034 * <p>
035 * The order ({@link #getOrder()}) of this Recombination implementation is two.
036 * </p>
037 *
038 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
039 * @since 1.0
040 * @version 4.4
041 */
042 public class MeanAlterer<
043 G extends Gene<?, G> & Mean<G>,
044 C extends Comparable<? super C>
045 >
046 extends Recombinator<G, C>
047 {
048
049 /**
050 * Constructs an alterer with a given recombination probability.
051 *
052 * @param probability the crossover probability.
053 * @throws IllegalArgumentException if the {@code probability} is not in the
054 * valid range of {@code [0, 1]}.
055 */
056 public MeanAlterer(final double probability) {
057 super(probability, 2);
058 }
059
060 /**
061 * Create a new alterer with alter probability of {@code 0.05}.
062 */
063 public MeanAlterer() {
064 this(0.05);
065 }
066
067 @Override
068 @SuppressWarnings("deprecation")
069 protected int recombine(
070 final MSeq<Phenotype<G, C>> population,
071 final int[] individuals,
072 final long generation
073 ) {
074 final Random random = RandomRegistry.getRandom();
075
076 final Phenotype<G, C> pt1 = population.get(individuals[0]);
077 final Phenotype<G, C> pt2 = population.get(individuals[1]);
078 final Genotype<G> gt1 = pt1.getGenotype();
079 final Genotype<G> gt2 = pt2.getGenotype();
080
081 //Choosing the Chromosome index for crossover.
082 final int cindex = random.nextInt(min(gt1.length(), gt2.length()));
083
084 final MSeq<Chromosome<G>> c1 = gt1.toSeq().copy();
085 final ISeq<Chromosome<G>> c2 = gt2.toSeq();
086
087 // Calculate the mean value of the gene array.
088 final MSeq<G> mean = mean(
089 c1.get(cindex).toSeq().copy(),
090 c2.get(cindex).toSeq()
091 );
092
093 c1.set(cindex, c1.get(cindex).newInstance(mean.toISeq()));
094
095 population.set(
096 individuals[0],
097 pt1.newInstance(Genotype.of(c1), generation)
098 );
099
100 return 1;
101 }
102
103 private static <G extends Gene<?, G> & Mean<G>>
104 MSeq<G> mean(final MSeq<G> a, final Seq<G> b) {
105 for (int i = a.length(); --i >= 0;) {
106 a.set(i, a.get(i).mean(b.get(i)));
107 }
108 return a;
109 }
110
111 @Override
112 public String toString() {
113 return format("%s[p=%f]", getClass().getSimpleName(), _probability);
114 }
115
116 }
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