ackminimizing.java

人工智能 作业3 武汉大学 国际软件学院

import java.io.*;

import org.jgap.*;
import org.jgap.data.*;
import org.jgap.impl.*;
import org.jgap.xml.*;
import org.w3c.dom.*;

public class ACKMinimizing {
	
	private final static String CVS_REVISION = "$Revision: 1.23 $";

	  /**
	   * The total number of times we'll let the population evolve.
	   */
	  private static final int MAX_ALLOWED_EVOLUTIONS = 200;
	  
	  
	  public static void makeChangeForAmount()
      throws Exception {
    // Start with a DefaultConfiguration, which comes setup with the
    // most common settings.
    // -------------------------------------------------------------
    Configuration conf = new DefaultConfiguration();
    conf.setPreservFittestIndividual(true);
    FitnessFunction myFunc =new ACKMinimizingFitnessFunction();
    conf.setFitnessFunction(myFunc);

    Gene[] myGenes = new Gene[30];
    
    for(int i=0;i<30;i++){
    	myGenes[i]=new DoubleGene(conf, -5.0, 5.0); 	
    }
    IChromosome myChromosome = new Chromosome(conf, myGenes);
    conf.setSampleChromosome(myChromosome);
    
    conf.setPopulationSize(1000);

    Genotype population;
    
    // Now we initialize the population randomly, anyway (as an example only)!
    // If you want to load previous results from file, remove the next line!
    // -----------------------------------------------------------------------
    population = Genotype.randomInitialGenotype(conf);
    // Evolve the population. Since we don't know what the best answer
    // is going to be, we just evolve the max number of times.
    // ---------------------------------------------------------------
    long startTime = System.currentTimeMillis();
    for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
      if (!uniqueChromosomes(population.getPopulation())) {
        throw new RuntimeException("Invalid state in generation "+i);
      }
      population.evolve();
    }
    long endTime = System.currentTimeMillis();
    System.out.println("Total evolution time: " + ( endTime - startTime)
                       + " ms");
    
    IChromosome bestSolutionSoFar = population.getFittestChromosome();
    System.out.println("The best solution has a fitness value of " +
                       bestSolutionSoFar.getFitnessValue());
    System.out.println("It contained the following: ");
    for(int i=0;i<30;i++){
    	System.out.println("\t" +
                ACKMinimizingFitnessFunction.
                getValueAtGene(
 bestSolutionSoFar, i));  	
    }
  }

  public static void main(String[] args)
      throws Exception {
	  makeChangeForAmount();    
  }	
  public static boolean uniqueChromosomes(Population a_pop) {
   
    for(int i=0;i<a_pop.size()-1;i++) {
      IChromosome c = a_pop.getChromosome(i);
      for(int j=i+1;j<a_pop.size();j++) {
        IChromosome c2 =a_pop.getChromosome(j);
        if (c == c2) {
          return false;
        }
      }
    }
    return true;
  }
}