jgapthread.java

一个开源的用java开发的遗传算法的封装好的工程

/*
 * 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 org.jgap.multithread;

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

/**
 * A single thread working with its own Configuration object
 *
 * @author Klaus Meffert
 * @since 3.2
 */
public class JGAPThread
    implements Runnable {
  /** String containing the CVS revision. Read out via reflection!*/
  private final static String CVS_REVISION = "$Revision: 1.0 $";

  private Configuration m_config;

  private Genotype m_genotype;
  private int m_index;

  public JGAPThread(int i) {
    m_index = i;
  }

  public void run() {
    try {
      String threadid = Thread.currentThread().getName();
      System.err.println("Starting thread with ID " + threadid);
      m_config = new DefaultConfiguration();
//      m_config.setFitnessEvaluator(new DefaultFitnessEvaluator());
      m_config.setFitnessFunction(new MaxFunction());
      m_config.setPreservFittestIndividual(false);
      m_config.setKeepPopulationSizeConstant(false);
      IChromosome sampleChromosome;
      if (m_index % 2 == 0) {
        sampleChromosome = new Chromosome(m_config,
            new IntegerGene(m_config, -50, +50), 20);
      }
      else {
        sampleChromosome = new Chromosome(m_config,
            new IntegerGene(m_config, -3, +3), 40);
      }
      m_config.setSampleChromosome(sampleChromosome);
      m_config.setPopulationSize(5);
      m_genotype = Genotype.randomInitialGenotype(m_config);
      int evolution = 0;
      IChromosome fittest;
      while (!Thread.currentThread().interrupted()) {
        doWork(m_genotype);
        // Pause to avoid 100% CPU load.
        // -----------------------------
        Thread.sleep(100);
        if (++evolution > 100) {
          break;
        }
        if (evolution % 10 == 0) {
          fittest = m_genotype.getFittestChromosome();
          System.out.println("[" + threadid +
                             "][Evolution "+evolution+
                             "] Current best solution has fitness " +
                             fittest.getFitnessValue());
        }
      }
      fittest = m_genotype.getFittestChromosome();
      System.out.println("[" + threadid + "] Best solution found has fitness " +
                         fittest.getFitnessValue());
    } catch (Exception ex) {
      ex.printStackTrace();
      System.exit(1);
    }
  }

  private void doWork(Genotype a_genotype) {
    a_genotype.evolve();
  }
}