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Java example source code file (ImageEvolutionExample.java)

This example Java source code file (ImageEvolutionExample.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

awt, bufferedimage, dimension, display, event, exception, geneticalgorithm, gui, image, imagepainter, jlabel, mutation_change, override, polygon_count, polygonchromosome, population_size, runnable, swing, thread, util

The ImageEvolutionExample.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.userguide.genetics;

import java.awt.Component;
import java.awt.Dimension;
import java.awt.FlowLayout;
import java.awt.Graphics;
import java.awt.Graphics2D;
import java.awt.event.ActionEvent;
import java.awt.event.ActionListener;
import java.awt.image.BufferedImage;
import java.io.File;
import java.io.IOException;
import java.util.LinkedList;
import java.util.List;

import javax.imageio.ImageIO;
import javax.swing.Box;
import javax.swing.ImageIcon;
import javax.swing.JButton;
import javax.swing.JLabel;

import org.apache.commons.math3.genetics.Chromosome;
import org.apache.commons.math3.genetics.ElitisticListPopulation;
import org.apache.commons.math3.genetics.GeneticAlgorithm;
import org.apache.commons.math3.genetics.Population;
import org.apache.commons.math3.genetics.TournamentSelection;
import org.apache.commons.math3.genetics.UniformCrossover;
import org.apache.commons.math3.userguide.ExampleUtils;
import org.apache.commons.math3.userguide.ExampleUtils.ExampleFrame;

 * This example shows a more advanced use of a genetic algorithm: approximate a raster image
 * with ~100 semi-transparent polygons of length 6.
 * <p>
 * The fitness function is quite simple yet expensive to compute:
 *   - draw the polygons of a chromosome to an image
 *   - compare each pixel with the corresponding reference image
 * <p>
 * To improve the speed of the calculation, we calculate the fitness not on the original image size,
 * but rather on a scaled down version, which is sufficient to demonstrate the power of such a genetic algorithm.
 * <p>
 * TODO:
 *  - improve user interface
 *    - make algorithm parameters configurable
 *    - add a gallery of results after x iterations / minutes (either automatic or based on button click)
 *    - allow loading / selection of other images
 *    - add logging in the user interface, e.g. number of generations, time spent, ...
 * @see <a href="http://www.nihilogic.dk/labs/evolving-images/">Evolving Images with JavaScript and canvas (Nihilogic)
public class ImageEvolutionExample {

    public static final int   POPULATION_SIZE  = 40;
    public static final int   TOURNAMENT_ARITY = 5;
    public static final float MUTATION_RATE    = 0.02f;
    public static final float MUTATION_CHANGE  = 0.1f;

    public static final int POLYGON_LENGTH = 6;
    public static final int POLYGON_COUNT = 100;

    public static class Display extends ExampleFrame {
        private GeneticAlgorithm ga;
        private Population currentPopulation;
        private Chromosome bestFit;

        private Thread internalThread;
        private volatile boolean noStopRequested;

        private BufferedImage ref;
        private BufferedImage referenceImage;
        private BufferedImage testImage;
        private ImagePainter painter;

        public Display() throws Exception {
            setTitle("Commons-Math: Image Evolution Example");
            setSize(600, 400);
            setLayout(new FlowLayout());

            Box bar = Box.createHorizontalBox();

            ref = ImageIO.read(new File("resources/monalisa.png"));
            //ref = ImageIO.read(new File("resources/feather-small.gif"));

            referenceImage = resizeImage(ref, 50, 50, BufferedImage.TYPE_INT_ARGB);
            testImage = new BufferedImage(referenceImage.getWidth(), referenceImage.getHeight(), BufferedImage.TYPE_INT_ARGB);

            JLabel picLabel = new JLabel(new ImageIcon(ref));

            painter = new ImagePainter(ref.getWidth(), ref.getHeight());

            // set the images used for calculating the fitness function:
            //   refImage  - the reference image
            //   testImage - the test image to draw the current chromosome


            JButton startButton = new JButton("Start");

            startButton.addActionListener(new ActionListener() {
                public void actionPerformed(ActionEvent e) {
                    if (isAlive()) {
                    } else {

            // initialize a new genetic algorithm
            ga = new GeneticAlgorithm(new UniformCrossover<Polygon>(0.5), 1.0,
                                      new RandomPolygonMutation(MUTATION_RATE, MUTATION_CHANGE), 1.0,
                                      new TournamentSelection(TOURNAMENT_ARITY));

            // initial population
            currentPopulation = getInitialPopulation();
            bestFit = currentPopulation.getFittestChromosome();
        public boolean isAlive() {
            return internalThread != null && internalThread.isAlive();

        public void stopRequest() {
            noStopRequested = false;
            internalThread = null;

        public void startEvolution() {
            noStopRequested = true;
            Runnable r = new Runnable() {
                public void run() {
                    int evolution = 0;
                    while (noStopRequested) {
                        currentPopulation = ga.nextGeneration(currentPopulation);

                        System.out.println("generation: " + evolution++ + ": " + bestFit.toString());
                        bestFit = currentPopulation.getFittestChromosome();


            internalThread = new Thread(r);

        private class ImagePainter extends Component {
            private int width;
            private int height;

            public ImagePainter(int width, int height) {
                this.width = width;
                this.height = height;

            public Dimension getPreferredSize() {
                return new Dimension(width, height);

            public Dimension getMinimumSize() {
                return getPreferredSize();

            public Dimension getMaximumSize() {
                return getPreferredSize();

            public void paint(Graphics g) {
                PolygonChromosome chromosome = (PolygonChromosome) bestFit;
                chromosome.draw((Graphics2D) g, ref.getWidth(), ref.getHeight());



    public static void main(String[] args) throws Exception {
        ExampleUtils.showExampleFrame(new Display());

    private static BufferedImage resizeImage(BufferedImage originalImage, int width, int height, int type) throws IOException {
        BufferedImage resizedImage = new BufferedImage(width, height, type);
        Graphics2D g = resizedImage.createGraphics();
        g.drawImage(originalImage, 0, 0, width, height, null);
        return resizedImage;

    private static Population getInitialPopulation() {
        List<Chromosome> popList = new LinkedList();
        for (int i = 0; i < POPULATION_SIZE; i++) {
            popList.add(PolygonChromosome.randomChromosome(POLYGON_LENGTH, POLYGON_COUNT));
        return new ElitisticListPopulation(popList, popList.size(), 0.25);


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