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Java example source code file (GeneticAlgorithmTestBinary.java)

This example Java source code file (GeneticAlgorithmTestBinary.java) is included in the alvinalexander.com "Java Source Code Warehouse" project. The intent of this project is to help you "Learn Java by Example" TM.

Learn more about this Java project at its project page.

Java - Java tags/keywords

binarychromosome, binarymutation, chromosome, crossover_rate, elitism_rate, elitisticlistpopulation, findones, geneticalgorithm, geneticalgorithmtestbinary, mutation_rate, onepointcrossover, population_size, stoppingcondition, tournamentselection, util

The GeneticAlgorithmTestBinary.java Java example 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,
 * See the License for the specific language governing permissions and
 * limitations under the License.
package org.apache.commons.math3.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;

    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(),
                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));
        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 {

        public FindOnes(List<Integer> representation) {

         * Returns number of elements != 0
        public double fitness() {
            int num = 0;
            for (int val : this.getRepresentation()) {
                if (val != 0)
            // number of elements >= 0
            return num;

        public AbstractListChromosome<Integer> newFixedLengthChromosome(List chromosomeRepresentation) {
            return new FindOnes(chromosomeRepresentation);


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