simpleexample.java

JGAP（发音"jay-gap"）是一款用Java编写的遗传算法包。提供了基本的遗传算法.你可以使用它来解决一些适用于遗传算法解决的问题.

/*
 * This file is part of JGAP.
 *
 * JGAP offers a dual license model containing the LGPL as well as the MPL.
 *
 * For licencing information please see the file license.txt included with JGAP
 * or have a look at the top of class org.jgap.Chromosome which representatively
 * includes the JGAP license policy applicable for any file delivered with JGAP.
 */
package examples.simpleBoolean;

import org.jgap.*;
import org.jgap.impl.*;

/**
 * Simple class that demonstrates the basic usage of JGAP.
 *
 * @author Neil Rotstan
 * @author Klaus Meffert
 * @since 2.0
 */
public class SimpleExample {
  /** String containing the CVS revision. Read out via reflection!*/
  private static final String CVS_REVISION = "$Revision: 1.7 $";

  /**
   * Starts the example
   * @param args ignored here
   */
  public static void main(String[] args) {
    int numEvolutions = 500;
    Configuration gaConf = new DefaultConfiguration();
    gaConf.setPreservFittestIndividual(true);
    gaConf.setKeepPopulationSizeConstant(false);
    Genotype genotype = null;
    int chromeSize;
    if (args.length > 0) {
      chromeSize = Integer.parseInt(args[0]);
    }
    else {
      chromeSize = 16;
    }
    double maxFitness = Math.pow(2.0, (double) chromeSize) - 1;
    if (chromeSize > 32) {
      System.err.println("This example does not handle " +
                         "Chromosomes greater than 32 bits in length.");
      System.exit( -1);
    }
    try {
      IChromosome sampleChromosome = new Chromosome(gaConf,
          new BooleanGene(gaConf), chromeSize);
      gaConf.setSampleChromosome(sampleChromosome);
      gaConf.setPopulationSize(20);
      gaConf.setFitnessFunction(new MaxFunction());
      genotype = Genotype.randomInitialGenotype(gaConf);
    }
    catch (InvalidConfigurationException e) {
      e.printStackTrace();
      System.exit( -2);
    }
    int progress = 0;
    int percentEvolution = numEvolutions / 100;
    for (int i = 0; i < numEvolutions; i++) {
      genotype.evolve();
      // Print progress.
      // ---------------
      if (percentEvolution > 0 && i % percentEvolution == 0) {
        progress++;
        IChromosome fittest = genotype.getFittestChromosome();
        double fitness = fittest.getFitnessValue();
        System.out.println("Currently fittest Chromosome has fitness " +
                           fitness);
        if (fitness >= maxFitness) {
          break;
        }
      }
    }
    // Print summary.
    // --------------
    IChromosome fittest = genotype.getFittestChromosome();
    System.out.println("Fittest Chromosome has fitness " +
                       fittest.getFitnessValue());
  }
}