abstractsupergenetest.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.supergene;

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

/**
 * Abstract class for testing Supergene performance.
 *
 * @author Neil Rotstan
 * @author Klaus Meffert
 * @author Audrius Meskauskas (subsequent adaptation)
 * @since 2.0
 * */
public abstract class AbstractSupergeneTest {
  /** String containing the CVS revision. Read out via reflection!*/
  private static final String CVS_REVISION = "$Revision: 1.3 $";

  private transient Configuration m_conf;

  /**
   * Gene index for the dimes gene
   */
  public static final int DIMES = 0;

  /**
   * Gene index for the quarters gene.
   */
  public static final int QUARTERS = 1;

  /**
   * Gene index for the nickels gene
   * Only used in the alternative presentation  */
  public static final int NICKELS = 2;

  /**
   * Gene index for the pennies gene.
   * Only used in the alternative presentation  */
  public static final int PENNIES = 3;

  /**
   * The total number of times we'll let the population evolve.
   */
  public static int MAX_ALLOWED_EVOLUTIONS = 200;

  /**
   * Chromosome size.
   */
  public static int POPULATION_SIZE = 2000;

  public static boolean REPORT_ENABLED = true;

  /**
   * @return created Dimes gene instance
   */
  protected Gene getDimesGene(Configuration a_conf) {
    try {
      return new IntegerGene(a_conf, 0, 2); // 10?
    }
    catch (InvalidConfigurationException iex) {
      throw new IllegalStateException(iex.getMessage());
    }
  };

  /**
   * @return created Nickels gene instance
   */
  protected Gene getNickelsGene(Configuration a_conf) {
    try {
      return new IntegerGene(a_conf, 0, 5);
    }
    catch (InvalidConfigurationException iex) {
      throw new IllegalStateException(iex.getMessage());
    }
  }

  /**
   * @return created Pennies (1) gene instance
   */
  protected Gene getPenniesGene(Configuration a_conf) {
    try {
      return new IntegerGene(a_conf, 0, 7);
    }
    catch (InvalidConfigurationException iex) {
      throw new IllegalStateException(iex.getMessage());
    }
  }

  /**
   * @return created Quarters gene instance
   */
  protected Gene getQuartersGene(Configuration a_conf) {
    try {
      return new IntegerGene(a_conf, 0, 3);
    }
    catch (InvalidConfigurationException iex) {
      throw new IllegalStateException(iex.getMessage());
    }
  }

  /** Compute the money value from the coin information. */
  public static int amountOfChange(int a_numQuarters, int a_numDimes,
                                   int a_numNickels, int a_numPennies) {
    return (a_numQuarters * 25) + (a_numDimes * 10) + (a_numNickels * 5)
        + a_numPennies;
  };

  /**
   * Executes the genetic algorithm to determine the minimum number of
   * coins necessary to make up the given target amount of change. The
   * solution will then be written to System.out.
   *
   * @param a_targetChangeAmount the target amount of change for which this
   * method is attempting to produce the minimum number of coins
   *
   * @return absolute difference between the required and computed change
   * amount
   * @throws Exception
   */
  public abstract int makeChangeForAmount(int a_targetChangeAmount)
      throws Exception;

  /**
   * Write report on eveluation to the given stream.
   * @param a_fitnessFunction p_SupergeneChangeFitnessFunction
   * @param a_population Genotype
   * @return Chromosome
   */
  public IChromosome report(SupergeneChangeFitnessFunction a_fitnessFunction,
                            Genotype a_population) {
    IChromosome bestSolutionSoFar = a_population.getFittestChromosome();
    if (!REPORT_ENABLED) {
      return bestSolutionSoFar;
    }
    System.out.println("\nThe best solution has a fitness value of "
                       + bestSolutionSoFar.getFitnessValue());
    System.out.println("It contained the following: ");
    System.out.println("\t" + a_fitnessFunction.getNumberOfCoinsAtGene(
        bestSolutionSoFar, QUARTERS) + " quarters.");
    System.out.println("\t" + a_fitnessFunction.getNumberOfCoinsAtGene(
        bestSolutionSoFar, DIMES) + " dimes.");
    System.out.println("\t" + a_fitnessFunction.getNumberOfCoinsAtGene(
        bestSolutionSoFar, NICKELS) + " nickels.");
    System.out.println("\t" + a_fitnessFunction.getNumberOfCoinsAtGene(
        bestSolutionSoFar, PENNIES) + " pennies.");
    System.out.println("For a total of " + a_fitnessFunction.amountOfChange(
        bestSolutionSoFar) + " cents in "
                       + a_fitnessFunction.getTotalNumberOfCoins(
        bestSolutionSoFar) + " coins.");
    return bestSolutionSoFar;
  }

  /**
   * If set to true (required for strict tests), only tasks with existing
   * solutions will be submitted as a test tasks.
   */
  public static boolean EXISTING_SOLUTIONS_ONLY = false;

  /**
   * Test the method, returns the sum of all differences between
   * the required and obtained excange amount. One exception counts
   * as 1000 on the error score.
   */
  public int test() {
    int s = 0;
    int e;
    Test:
        for (int amount = 20; amount < 100; amount++) {
      try {
        if (REPORT_ENABLED) {
          System.out.println("EXCANGING " + amount + " ");
        }
        // Do not solve cases without solutions
        if (EXISTING_SOLUTIONS_ONLY) {
          if (!Force.solve(amount)) {
            continue Test;
          }
        }
        // Need to reset the configuration because it needs to be changed each
        // time when looping.
        // -------------------------------------------------------------------
        DefaultConfiguration.reset();
        e = makeChangeForAmount(amount);
        if (REPORT_ENABLED) {
          System.out.println(" err " + e);
          System.out.println("---------------");
        }
        s = s + e;
      }
      catch (Exception ex) {
        ex.printStackTrace();
        s += 1000;
      }
    }
    if (REPORT_ENABLED) {
      System.out.println("Sum of errors " + s);
    }
    return s;
  }

  /**
   * Find and print the solution, return the solution error.
   * @return absolute difference between the required and computed change
   */
  protected int solve(Configuration a_conf, int a_targetChangeAmount,
                      SupergeneChangeFitnessFunction a_fitnessFunction,
                      Gene[] a_sampleGenes)
      throws InvalidConfigurationException {
    IChromosome sampleChromosome = new Chromosome(a_conf, a_sampleGenes);
    a_conf.setSampleChromosome(sampleChromosome);
    // Finally, we need to tell the Configuration object how many
    // Chromosomes we want in our population. The more Chromosomes,
    // the larger number of potential solutions (which is good for
    // finding the answer), but the longer it will take to evolve
    // the population (which could be seen as bad). We'll just set
    // the population size to 500 here.
    // ------------------------------------------------------------
    a_conf.setPopulationSize(POPULATION_SIZE);
    // Create random initial population of Chromosomes.
    // ------------------------------------------------
    Genotype population = Genotype.randomInitialGenotype(a_conf);
    int s;
    Evolution:
        // Evolve the population, break if the the change solution is found.
        // -----------------------------------------------------------------
        for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
      population.evolve();
      s = Math.abs(a_fitnessFunction.amountOfChange(population.
          getFittestChromosome())
                   - a_targetChangeAmount);
      if (s == 0) {
        break Evolution;
      }
    }
    // Display the best solution we found.
    // -----------------------------------
    IChromosome bestSolutionSoFar = report(a_fitnessFunction, population);
    return Math.abs(a_fitnessFunction.amountOfChange(bestSolutionSoFar)
                    - a_targetChangeAmount);
  }

}