コード例 #1
0
  /**
   * 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
   * @param a_doMonitor true: turn on monitoring for later evaluation of evolution progress
   * @throws Exception
   * @author Neil Rotstan
   * @author Klaus Meffert
   * @since 1.0
   */
  public static void makeChangeForAmount(int a_targetChangeAmount, boolean a_doMonitor)
      throws Exception {
    // Start with a DefaultConfiguration, which comes setup with the
    // most common settings.
    // -------------------------------------------------------------
    Configuration conf = new DefaultConfiguration();
    // Care that the fittest individual of the current population is
    // always taken to the next generation.
    // Consider: With that, the pop. size may exceed its original
    // size by one sometimes!
    // -------------------------------------------------------------
    conf.setPreservFittestIndividual(true);
    conf.setKeepPopulationSizeConstant(false);
    // Set the fitness function we want to use, which is our
    // MinimizingMakeChangeFitnessFunction. We construct it with
    // the target amount of change passed in to this method.
    // ---------------------------------------------------------
    FitnessFunction myFunc = new SampleFitnessFunction(a_targetChangeAmount);
    conf.setFitnessFunction(myFunc);
    if (a_doMonitor) {
      // Turn on monitoring/auditing of evolution progress.
      // --------------------------------------------------
      m_monitor = new EvolutionMonitor();
      conf.setMonitor(m_monitor);
    }
    // Now we need to tell the Configuration object how we want our
    // Chromosomes to be setup. We do that by actually creating a
    // sample Chromosome and then setting it on the Configuration
    // object. As mentioned earlier, we want our Chromosomes to each
    // have four genes, one for each of the coin types. We want the
    // values (alleles) of those genes to be integers, which represent
    // how many coins of that type we have. We therefore use the
    // IntegerGene class to represent each of the genes. That class
    // also lets us specify a lower and upper bound, which we set
    // to sensible values for each coin type.
    // --------------------------------------------------------------
    Gene[] sampleGenes = new Gene[4];
    sampleGenes[0] = new IntegerGene(conf, 0, 98); // Wasser
    sampleGenes[1] = new IntegerGene(conf, 0, 98); // Zucker
    sampleGenes[2] = new IntegerGene(conf, 0, 98); // Saft1
    sampleGenes[3] = new IntegerGene(conf, 0, 98); // Saft2
    IChromosome sampleChromosome = new Chromosome(conf, sampleGenes);
    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).
    // ------------------------------------------------------------
    conf.setPopulationSize(80);

    // Now we initialize the population randomly, anyway (as an example only)!
    // If you want to load previous results from file, remove the next line!
    // -----------------------------------------------------------------------
    Genotype 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);
      }
      if (m_monitor != null) {
        population.evolve(m_monitor);
      } else {
        population.evolve();
      }
    }
    long endTime = System.currentTimeMillis();
    System.out.println("Total evolution time: " + (endTime - startTime) + " ms");
    // Save progress to file. A new run of this example will then be able to
    // resume where it stopped before! --> this is completely optional.
    // ---------------------------------------------------------------------

    // Display the best solution we found.
    // -----------------------------------
    IChromosome bestSolutionSoFar = population.getFittestChromosome();
    double v1 = bestSolutionSoFar.getFitnessValue();
    System.out.println(
        "The best solution has a fitness value of " + bestSolutionSoFar.getFitnessValue());
    bestSolutionSoFar.setFitnessValueDirectly(-1);
    System.out.println("It contains the following: ");
    System.out.println(
        "\t" + SampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 0) + " ml water.");
    System.out.println(
        "\t" + SampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 1) + " ml sugar.");
    System.out.println(
        "\t" + SampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 2) + " ml juice 1.");
    System.out.println(
        "\t" + SampleFitnessFunction.getNumberOfCoinsAtGene(bestSolutionSoFar, 3) + " ml juice 2.");
    System.out.println(
        "For a total of " + SampleFitnessFunction.amountOfChange(bestSolutionSoFar) + " ml.");
  }
コード例 #2
0
  /**
   * Executes the genetic algorithm to determine the minimum number of items necessary to make up
   * the given target volume. The solution will then be written to the console.
   *
   * @param a_knapsackVolume the target volume for which this method is attempting to produce the
   *     optimal list of items
   * @throws Exception
   * @author Klaus Meffert
   * @since 2.3
   */
  public static void findItemsForVolume(double a_knapsackVolume) throws Exception {
    // Start with a DefaultConfiguration, which comes setup with the
    // most common settings.
    // -------------------------------------------------------------
    Configuration conf = new DefaultConfiguration();
    conf.setPreservFittestIndividual(true);
    // Set the fitness function we want to use. We construct it with
    // the target volume passed in to this method.
    // ---------------------------------------------------------
    FitnessFunction myFunc = new KnapsackFitnessFunction(a_knapsackVolume);
    conf.setFitnessFunction(myFunc);
    // Now we need to tell the Configuration object how we want our
    // Chromosomes to be setup. We do that by actually creating a
    // sample Chromosome and then setting it on the Configuration
    // object. As mentioned earlier, we want our Chromosomes to each
    // have as many genes as there are different items available. We want the
    // values (alleles) of those genes to be integers, which represent
    // how many items of that type we have. We therefore use the
    // IntegerGene class to represent each of the genes. That class
    // also lets us specify a lower and upper bound, which we set
    // to senseful values (i.e. maximum possible) for each item type.
    // --------------------------------------------------------------
    Gene[] sampleGenes = new Gene[itemVolumes.length];
    for (int i = 0; i < itemVolumes.length; i++) {
      sampleGenes[i] = new IntegerGene(conf, 0, (int) Math.ceil(a_knapsackVolume / itemVolumes[i]));
    }
    IChromosome sampleChromosome = new Chromosome(conf, sampleGenes);
    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).
    // ------------------------------------------------------------
    conf.setPopulationSize(50);
    // Create random initial population of Chromosomes.
    // Here we try to read in a previous run via XMLManager.readFile(..)
    // for demonstration purpose!
    // -----------------------------------------------------------------
    Genotype population;
    try {
      Document doc = XMLManager.readFile(new File("knapsackJGAP.xml"));
      population = XMLManager.getGenotypeFromDocument(conf, doc);
    } catch (FileNotFoundException fex) {
      population = Genotype.randomInitialGenotype(conf);
    }
    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.
    // ---------------------------------------------------------------
    for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
      population.evolve();
    }
    // Save progress to file. A new run of this example will then be able to
    // resume where it stopped before!
    // ---------------------------------------------------------------------

    // represent Genotype as tree with elements Chromomes and Genes
    // ------------------------------------------------------------
    DataTreeBuilder builder = DataTreeBuilder.getInstance();
    IDataCreators doc2 = builder.representGenotypeAsDocument(population);
    // create XML document from generated tree
    // ---------------------------------------
    XMLDocumentBuilder docbuilder = new XMLDocumentBuilder();
    Document xmlDoc = (Document) docbuilder.buildDocument(doc2);
    XMLManager.writeFile(xmlDoc, new File("knapsackJGAP.xml"));
    // Display the best solution we found.
    // -----------------------------------
    IChromosome bestSolutionSoFar = population.getFittestChromosome();
    System.out.println(
        "The best solution has a fitness value of " + bestSolutionSoFar.getFitnessValue());
    System.out.println("It contained the following: ");
    int count;
    double totalVolume = 0.0d;
    for (int i = 0; i < bestSolutionSoFar.size(); i++) {
      count = ((Integer) bestSolutionSoFar.getGene(i).getAllele()).intValue();
      if (count > 0) {
        System.out.println("\t " + count + " x " + itemNames[i]);
        totalVolume += itemVolumes[i] * count;
      }
    }
    System.out.println("\nFor a total volume of " + totalVolume + " ccm");
    System.out.println("Expected volume was " + a_knapsackVolume + " ccm");
    System.out.println("Volume difference is " + Math.abs(totalVolume - a_knapsackVolume) + " ccm");
  }
コード例 #3
0
  /**
   * Executes the genetic algorithm to determine the minimum number of items necessary to make up
   * the given target volume. The solution will then be written to the console.
   *
   * @param a_knapsackVolume the target volume for which this method is attempting to produce the
   *     optimal list of items
   * @throws Exception
   * @author Klaus Meffert
   * @throws InvalidConfigurationException
   * @since 2.3
   */
  public static void findeBesteParameter() throws InvalidConfigurationException {
    // Start with a DefaultConfiguration, which comes setup with the
    // most common settings.
    // -------------------------------------------------------------
    Configuration conf = new myConfiguration();
    conf.setPreservFittestIndividual(true);

    // TODO: check: viel Mehraufwand?? ja!
    // conf.setAlwaysCaculateFitness(true);

    // Set the fitness function we want to use. We construct it with
    // the target volume passed in to this method.
    // ---------------------------------------------------------
    FitnessFunction myFunc = new EAmyTeamFitnessFunction();
    conf.setFitnessFunction(myFunc);

    // --> myConfiguration
    // conf.addGeneticOperator(new MutationOperator(conf,2));
    // conf.addGeneticOperator(new CrossoverOperator(conf, .2));

    // Now we need to tell the Configuration object how we want our
    // Chromosomes to be setup. We do that by actually creating a
    // sample Chromosome and then setting it on the Configuration
    // object.

    myTeamParameters dummyParam = new myTeamParameters(); // nur fuer min/max

    Gene[] sampleGenes = new Gene[myTeamParameters.ANZAHL_PARAMETER];
    for (int i = 0; i < sampleGenes.length; i++) {
      sampleGenes[i] = new DoubleGene(conf, dummyParam.getMin(i), dummyParam.getMax(i));
    }
    IChromosome sampleChromosome = new Chromosome(conf, sampleGenes);
    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).
    // ------------------------------------------------------------
    conf.setPopulationSize(POPULATION_SIZE);

    // Create random initial population of Chromosomes.
    // Here we try to read in a previous run via XMLManager.readFile(..)
    // for demonstration purpose!
    // -----------------------------------------------------------------
    Genotype population;
    try {
      Document doc = XMLManager.readFile(new File(XML_FILENAME));
      population = XMLManager.getGenotypeFromDocument(conf, doc);
      // TODO mit zufaelligen auffuellen??
      System.out.println("Alte Population aus Datei gelesen!");
    } catch (Exception fex) {
      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.
    // ---------------------------------------------------------------

    for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
      System.out.println("\nPopulation Nr. " + i + ":");
      printPopulation(population);

      population.evolve();
    }

    // Save progress to file. A new run of this example will then be able to
    // resume where it stopped before!
    // ---------------------------------------------------------------------

    // represent Genotype as tree with elements Chromomes and Genes
    // ------------------------------------------------------------
    try {
      DataTreeBuilder builder = DataTreeBuilder.getInstance();
      IDataCreators doc2 = builder.representGenotypeAsDocument(population);
      // create XML document from generated tree
      // ---------------------------------------
      XMLDocumentBuilder docbuilder = new XMLDocumentBuilder();
      Document xmlDoc = (Document) docbuilder.buildDocument(doc2);
      XMLManager.writeFile(xmlDoc, new File(XML_FILENAME));
    } catch (Exception e) {
      e.printStackTrace();
    }
    // Display the best solution we found.
    // -----------------------------------
    IChromosome bestSolutionSoFar = population.getFittestChromosome();
    System.out.println(
        "The best solution has a fitness value of "
            + bestSolutionSoFar.getFitnessValueDirectly()
            + " mit folgenden Parametern:");
    printChromosom(bestSolutionSoFar, true);
  }