/*
* Java Genetic Algorithm Library (@__identifier__@).
* Copyright (c) @__year__@ Franz Wilhelmstötter
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
* You may obtain a copy of the License at
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing, software
* distributed under the License is distributed on an "AS IS" BASIS,
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
* See the License for the specific language governing permissions and
* limitations under the License.
*
* Author:
* Franz Wilhelmstötter (franz.wilhelmstoetter@gmx.at)
*/
package org.jenetics;
import static org.jenetics.TestUtils.newDoubleGenePopulation;
import static org.jenetics.stat.StatisticsAssert.assertDistribution;
import java.util.Random;
import org.testng.Assert;
import org.testng.annotations.DataProvider;
import org.testng.annotations.Test;
import org.jenetics.stat.Histogram;
import org.jenetics.stat.NormalDistribution;
import org.jenetics.stat.Variance;
import org.jenetics.util.CharSeq;
import org.jenetics.util.ISeq;
import org.jenetics.util.MSeq;
import org.jenetics.util.RandomRegistry;
import org.jenetics.util.Range;
import org.jenetics.util.Scoped;
/**
* @author <a href="mailto:franz.wilhelmstoetter@gmx.at">Franz Wilhelmstötter</a>
* @version <em>$Date$</em>
*/
public class SinglePointCrossoverTest {
private static final class ConstRandom extends Random {
private static final long serialVersionUID = 1L;
private final int _value;
public ConstRandom(final int value) {
_value = value;
}
@Override
public int nextInt() {
return _value;
}
@Override
public int nextInt(int n) {
return _value;
}
}
@Test
public void crossover() {
final CharSeq chars = CharSeq.of("a-zA-Z");
final ISeq<CharacterGene> g1 = new CharacterChromosome(chars, 20).toSeq();
final ISeq<CharacterGene> g2 = new CharacterChromosome(chars, 20).toSeq();
int rv = 12;
try (Scoped<?> s = RandomRegistry.scope(new ConstRandom(rv))) {
final SinglePointCrossover<CharacterGene>
crossover = new SinglePointCrossover<>();
MSeq<CharacterGene> g1c = g1.copy();
MSeq<CharacterGene> g2c = g2.copy();
crossover.crossover(g1c, g2c);
Assert.assertEquals(g1c.subSeq(0, rv), g1.subSeq(0, rv));
Assert.assertEquals(g1c.subSeq(rv), g2.subSeq(rv));
Assert.assertNotEquals(g1c, g2);
Assert.assertNotEquals(g2c, g1);
rv = 0;
try (Scoped<?> s2 = RandomRegistry.scope(new ConstRandom(rv))) {
g1c = g1.copy();
g2c = g2.copy();
crossover.crossover(g1c, g2c);
Assert.assertEquals(g1c, g2);
Assert.assertEquals(g2c, g1);
Assert.assertEquals(g1c.subSeq(0, rv), g1.subSeq(0, rv));
Assert.assertEquals(g1c.subSeq(rv), g2.subSeq(rv));
rv = 1;
try (Scoped<?> s3 = RandomRegistry.scope(new ConstRandom(rv))) {
g1c = g1.copy();
g2c = g2.copy();
crossover.crossover(g1c, g2c);
Assert.assertEquals(g1c.subSeq(0, rv), g1.subSeq(0, rv));
Assert.assertEquals(g1c.subSeq(rv), g2.subSeq(rv));
rv = g1.length();
try (Scoped<?> s4 = RandomRegistry.scope(new ConstRandom(rv))) {
g1c = g1.copy();
g2c = g2.copy();
crossover.crossover(g1c, g2c);
Assert.assertEquals(g1c, g1);
Assert.assertEquals(g2c, g2);
Assert.assertEquals(g1c.subSeq(0, rv), g1.subSeq(0, rv));
Assert.assertEquals(g1c.subSeq(rv), g2.subSeq(rv));
}
}
}
}
}
@Test(dataProvider = "alterProbabilityParameters")
public void alterProbability(
final Integer ngenes,
final Integer nchromosomes,
final Integer npopulation,
final Double p
) {
final Population<DoubleGene, Double> population = newDoubleGenePopulation(
ngenes, nchromosomes, npopulation
);
// The mutator to test.
final SinglePointCrossover<DoubleGene> crossover = new SinglePointCrossover<>(p);
final long nallgenes = ngenes*nchromosomes*npopulation;
final long N = 200;
final double mean = crossover.getOrder()*npopulation*p;
final long min = 0;
final long max = nallgenes;
final Range<Long> domain = new Range<>(min, max);
final Histogram<Long> histogram = Histogram.of(min, max, 10);
final Variance<Long> variance = new Variance<>();
for (int i = 0; i < N; ++i) {
final long alterations = crossover.alter(population, 1);
histogram.accumulate(alterations);
variance.accumulate(alterations);
}
// Normal distribution as approximation for binomial distribution.
assertDistribution(histogram, new NormalDistribution<>(domain, mean, variance.getVariance()));
}
@DataProvider(name = "alterProbabilityParameters")
public Object[][] alterProbabilityParameters() {
return TestUtils.alterProbabilityParameters();
}
}