| /* |
| * Licensed to the Apache Software Foundation (ASF) under one or more |
| * contributor license agreements. See the NOTICE file distributed with |
| * this work for additional information regarding copyright ownership. |
| * The ASF licenses this file to You 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. |
| */ |
| package org.apache.commons.math4.legacy.genetics; |
| |
| |
| import java.util.LinkedList; |
| import java.util.List; |
| |
| import org.junit.Assert; |
| import org.junit.Test; |
| |
| /** |
| * This is also an example of usage. |
| */ |
| public class GeneticAlgorithmTestBinary { |
| |
| // parameters for the GA |
| private static final int DIMENSION = 50; |
| private static final int POPULATION_SIZE = 50; |
| private static final int NUM_GENERATIONS = 50; |
| private static final double ELITISM_RATE = 0.2; |
| private static final double CROSSOVER_RATE = 1; |
| private static final double MUTATION_RATE = 0.1; |
| private static final int TOURNAMENT_ARITY = 2; |
| |
| @Test |
| public void test() { |
| // to test a stochastic algorithm is hard, so this will rather be an usage example |
| |
| // initialize a new genetic algorithm |
| GeneticAlgorithm ga = new GeneticAlgorithm( |
| new OnePointCrossover<Integer>(), |
| CROSSOVER_RATE, // all selected chromosomes will be recombined (=crossover) |
| new BinaryMutation(), |
| MUTATION_RATE, |
| new TournamentSelection(TOURNAMENT_ARITY) |
| ); |
| |
| Assert.assertEquals(0, ga.getGenerationsEvolved()); |
| |
| // initial population |
| Population initial = randomPopulation(); |
| // stopping conditions |
| StoppingCondition stopCond = new FixedGenerationCount(NUM_GENERATIONS); |
| |
| // best initial chromosome |
| Chromosome bestInitial = initial.getFittestChromosome(); |
| |
| // run the algorithm |
| Population finalPopulation = ga.evolve(initial, stopCond); |
| |
| // best chromosome from the final population |
| Chromosome bestFinal = finalPopulation.getFittestChromosome(); |
| |
| // the only thing we can test is whether the final solution is not worse than the initial one |
| // however, for some implementations of GA, this need not be true :) |
| |
| Assert.assertTrue(bestFinal.compareTo(bestInitial) > 0); |
| Assert.assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved()); |
| |
| } |
| |
| |
| |
| |
| /** |
| * Initializes a random population. |
| */ |
| private static ElitisticListPopulation randomPopulation() { |
| List<Chromosome> popList = new LinkedList<>(); |
| |
| for (int i=0; i<POPULATION_SIZE; i++) { |
| BinaryChromosome randChrom = new FindOnes(BinaryChromosome.randomBinaryRepresentation(DIMENSION)); |
| popList.add(randChrom); |
| } |
| return new ElitisticListPopulation(popList, popList.size(), ELITISM_RATE); |
| } |
| |
| /** |
| * Chromosomes represented by a binary chromosome. |
| * |
| * The goal is to set all bits (genes) to 1. |
| */ |
| private static class FindOnes extends BinaryChromosome { |
| |
| FindOnes(List<Integer> representation) { |
| super(representation); |
| } |
| |
| /** |
| * Returns number of elements != 0 |
| */ |
| @Override |
| public double fitness() { |
| int num = 0; |
| for (int val : this.getRepresentation()) { |
| if (val != 0) { |
| num++; |
| } |
| } |
| // number of elements >= 0 |
| return num; |
| } |
| |
| @Override |
| public AbstractListChromosome<Integer> newFixedLengthChromosome(List<Integer> chromosomeRepresentation) { |
| return new FindOnes(chromosomeRepresentation); |
| } |
| |
| } |
| } |