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 */
020 package io.jenetics;
021
022 import static java.lang.Math.min;
023 import static java.lang.String.format;
024
025 import io.jenetics.internal.util.Requires;
026 import io.jenetics.util.MSeq;
027 import io.jenetics.util.RandomRegistry;
028
029 /**
030 * This alterer takes two chromosomes (treating it as vectors) and creates a
031 * linear combination of these vectors as a result. The line-recombination depends
032 * on a variable <em>p</em> which determines how far out along the line (defined
033 * by the two multidimensional points/vectors) the children are allowed to be.
034 * If <em>p</em> = 0 then the children will be located along the line within the
035 * hypercube between the two points. If <em>p</em> > 0 then the children may
036 * be located anywhere on the line, even somewhat outside the hypercube.
037 * <p>
038 * Points outside the allowed numeric range are rejected and the original
039 * value is used instead. The strategy on how out-of-range points are handled,
040 * is the difference to the very similar {@link IntermediateCrossover}.
041 *
042 * @see <a href="https://cs.gmu.edu/~sean/book/metaheuristics/"><em>
043 * Essentials of Metaheuristic, page 42</em></a>
044 * @see IntermediateCrossover
045 *
046 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
047 * @version 3.8
048 * @since 3.8
049 */
050 public class LineCrossover<
051 G extends NumericGene<?, G>,
052 C extends Comparable<? super C>
053 >
054 extends Crossover<G, C>
055 {
056
057 private final double _p;
058
059 /**
060 * Creates a new linear-crossover with the given recombination
061 * probability and the line-scaling factor <em>p</em>.
062 *
063 * @param probability the recombination probability.
064 * @param p defines the possible location of the recombined chromosomes. If
065 * <em>p</em> = 0 then the children will be located along the line
066 * within the hypercube between the two points. If <em>p</em> > 0
067 * then the children may be located anywhere on the line, even
068 * somewhat outside the hypercube.
069 * @throws IllegalArgumentException if the {@code probability} is not in the
070 * valid range of {@code [0, 1]} or if {@code p} is smaller then zero
071 */
072 public LineCrossover(final double probability, final double p) {
073 super(probability);
074 _p = Requires.nonNegative(p, "p");
075 }
076
077 /**
078 * Creates a new linear-crossover with the given recombination
079 * probability. The parameter <em>p</em> is set to zero, which restricts the
080 * recombined chromosomes within the hypercube of the selected chromosomes
081 * (vectors).
082 *
083 * @param probability the recombination probability.
084 * @throws IllegalArgumentException if the {@code probability} is not in the
085 * valid range of {@code [0, 1]}
086 */
087 public LineCrossover(final double probability) {
088 this(probability, 0);
089 }
090
091 /**
092 * Creates a new linear-crossover with default recombination
093 * probability ({@link #DEFAULT_ALTER_PROBABILITY}) and a <em>p</em> value
094 * of zero, which restricts the recombined chromosomes within the hypercube
095 * of the selected chromosomes (vectors).
096 */
097 public LineCrossover() {
098 this(DEFAULT_ALTER_PROBABILITY, 0);
099 }
100
101 @Override
102 protected int crossover(final MSeq<G> v, final MSeq<G> w) {
103 final var random = RandomRegistry.random();
104
105 final double min = v.get(0).min().doubleValue();
106 final double max = v.get(0).max().doubleValue();
107
108 final double a = random.nextDouble(-_p, 1 + _p);
109 final double b = random.nextDouble(-_p, 1 + _p);
110
111 boolean changed = false;
112 for (int i = 0, n = min(v.length(), w.length()); i < n; ++i) {
113 final double vi = v.get(i).doubleValue();
114 final double wi = w.get(i).doubleValue();
115
116 final double t = a*vi + (1 - a)*wi;
117 final double s = b*wi + (1 - b)*vi;
118
119 if (t >= min && s >= min && t < max && s < max) {
120 v.set(i, v.get(i).newInstance(t));
121 w.set(i, w.get(i).newInstance(s));
122 changed = true;
123 }
124 }
125
126 return changed ? 2 : 0;
127 }
128
129 @Override
130 public String toString() {
131 return format("%s[p=%f]", getClass().getSimpleName(), _probability);
132 }
133
134 }
|