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Commons Math example source code file (GeneticAlgorithmTestBinary.java)
The Commons Math GeneticAlgorithmTestBinary.java source code/* * 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.math.genetics; import static org.junit.Assert.*; import java.util.LinkedList; import java.util.List; 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 (=crosssover) new BinaryMutation(), MUTATION_RATE, new TournamentSelection(TOURNAMENT_ARITY) ); 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 :) assertTrue(bestFinal.compareTo(bestInitial) > 0); assertEquals(NUM_GENERATIONS, ga.getGenerationsEvolved()); } /** * Initializes a random population. */ private static ElitisticListPopulation randomPopulation() { List<Chromosome> popList = new LinkedList Other Commons Math examples (source code examples)Here is a short list of links related to this Commons Math GeneticAlgorithmTestBinary.java source code file: |
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