001 /*
002 * Java Genetic Algorithm Library (jenetics-4.0.0).
003 * Copyright (c) 2007-2017 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.String.format;
023 import static java.util.Objects.requireNonNull;
024
025 import java.util.Random;
026
027 import io.jenetics.internal.util.Equality;
028 import io.jenetics.internal.util.Hash;
029 import io.jenetics.util.ISeq;
030 import io.jenetics.util.MSeq;
031 import io.jenetics.util.RandomRegistry;
032 import io.jenetics.util.Seq;
033
034 /**
035 * The Monte Carlo selector selects the individuals from a given population
036 * randomly. This selector can be used to measure the performance of a other
037 * selectors. In general, the performance of a selector should be better than
038 * the selection performance of the Monte Carlo selector.
039 *
040 * @author <a href="mailto:franz.wilhelmstoetter@gmail.com">Franz Wilhelmstötter</a>
041 * @since 1.0
042 * @version 4.0
043 */
044 public final class MonteCarloSelector<
045 G extends Gene<?, G>,
046 C extends Comparable<? super C>
047 >
048 implements Selector<G, C>
049 {
050
051 public MonteCarloSelector() {
052 }
053
054 @Override
055 public ISeq<Phenotype<G, C>> select(
056 final Seq<Phenotype<G, C>> population,
057 final int count,
058 final Optimize opt
059 ) {
060 requireNonNull(population, "Population");
061 requireNonNull(opt, "Optimization");
062 if (count < 0) {
063 throw new IllegalArgumentException(format(
064 "Selection count must be greater or equal then zero, but was %d.",
065 count
066 ));
067 }
068
069 final MSeq<Phenotype<G, C>> selection = MSeq
070 .ofLength(population.isEmpty() ? 0 : count);
071
072 if (count > 0 && !population.isEmpty()) {
073 final Random random = RandomRegistry.getRandom();
074 final int size = population.size();
075
076 for (int i = 0; i < count; ++i) {
077 final int pos = random.nextInt(size);
078 selection.set(i, population.get(pos));
079 }
080 }
081
082 return selection.toISeq();
083 }
084
085 @Override
086 public int hashCode() {
087 return Hash.of(getClass()).value();
088 }
089
090 @Override
091 public boolean equals(final Object obj) {
092 return Equality.ofType(this, obj);
093 }
094
095 @Override
096 public String toString() {
097 return format("%s", getClass().getSimpleName());
098 }
099
100 }
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