maxfunction.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.*;

/**
 * Fitness function for our example. See evolve() method for details
 *
 * @author Neil Rotstan
 * @author Klaus Meffert
 * @since 2.0
 */
public class MaxFunction
    extends FitnessFunction {
  /** String containing the CVS revision. Read out via reflection!*/
  private final static String CVS_REVISION = "$Revision: 1.4 $";

  /**
   * This example implementation calculates the fitness value of Chromosomes
   * using BooleanAllele implementations. It simply returns a fitness value
   * equal to the numeric binary value of the bits. In other words, it
   * optimizes the numeric value of the genes interpreted as bits. It should
   * be noted that, for clarity, this function literally returns the binary
   * value of the Chromosome's genes interpreted as bits. However, it would
   * be better to return the value raised to a fixed power to exaggerate the
   * difference between the higher values. For example, the difference
   * between 254 and 255 is only about .04%, which isn't much incentive for
   * the selector to choose 255 over 254. However, if you square the values,
   * you then get 64516 and 65025, which is a difference of 0.8%--twice
   * as much and, therefore, twice the incentive to select the higher
   * value.
   * @param a_subject the Chromosome to be evaluated
   * @return defect rate of our problem
   *
   * @author Neil Rotstan
   * @author Klaus Meffert
   * @since 2.0
   */
  public double evaluate(IChromosome a_subject) {
    int total = 0;

    for (int i = 0; i < a_subject.size(); i++) {
      BooleanGene value = (BooleanGene) a_subject.getGene(a_subject.size() -
          (i + 1));
      if (value.booleanValue()) {
        total += Math.pow(2.0, (double) i);
      }
    }

    return total;
  }
}