Esempio n. 1
0
  public static void main(String[] args) throws Exception {
    // Start with a DefaultConfiguration, which comes setup with the
    // most common settings.
    // -------------------------------------------------------------
    Configuration conf = new DefaultConfiguration();

    // Set the fitness function we want to use, which is our
    // MinimizingMakeChangeFitnessFunction that we created earlier.
    // We construct it with the target amount of change provided
    // by the user.
    // ------------------------------------------------------------
    int targetAmount = 88;
    FitnessFunction myFunc = new MinimizingMakeChangeFitnessFunction(targetAmount);

    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 four genes, one for each of the coin types. We
    // want the values 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, 3); // Quarters
    sampleGenes[1] = new IntegerGene(conf, 0, 2); // Dimes
    sampleGenes[2] = new IntegerGene(conf, 0, 1); // Nickels
    sampleGenes[3] = new IntegerGene(conf, 0, 4); // Pennies

    Chromosome 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 the number of potential solutions (which is good
    // for finding the answer), but the longer it will take to evolve
    // the population each round. We'll set the population size to
    // 500 here.
    // --------------------------------------------------------------
    conf.setPopulationSize(10);

    Genotype population = Genotype.randomInitialGenotype(conf);
    for (int i = 0; i < MAX_ALLOWED_EVOLUTIONS; i++) {
      population.evolve();
      IChromosome partialSolution = population.getFittestChromosome();
      System.out.println("Solución en iteracion nro " + i + " : ");
      printStatus(partialSolution);
    }
    IChromosome bestSolutionSoFar = population.getFittestChromosome();

    System.out.println("Solución final: ");
    printStatus(bestSolutionSoFar);
  }
 /**
  * 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;
 }
 /**
  * Gibt die Population auf der Konsole aus.
  *
  * @param population
  */
 public static void printPopulation(Genotype population) {
   System.out.println("ERSTER\tF_VORNE\tGEG_UEB\tVERFOLG\t" + "WEG_GEG\tINS_HAU\tSCHLAGEN");
   for (Object chromosom : population.getPopulation().getChromosomes()) {
     printChromosom(
         (IChromosome) chromosom,
         ((IChromosome) chromosom).getFitnessValueDirectly()
                 >= population.getFittestChromosome().getFitnessValueDirectly()
             ? true
             : false);
   }
 }
 /**
  * Find and print the solution, return the solution error.
  *
  * @param a_conf the configuration to use
  * @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);
 }
  /**
   * 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");
  }
  /**
   * 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.");
  }
  /**
   * 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);
  }
Esempio n. 8
0
  public static void main(String[] args) throws InvalidConfigurationException {

    // Reading data from xml
    try {
      new InputData().readFromFile(XML_TEST_FILENAME);
    } catch (SAXException e) {
      System.out.println(e.getMessage());
    } catch (IOException e) {
      System.out.println(e.getMessage());
    } catch (ParserConfigurationException e) {
      System.out.println(e.getMessage());
    }

    // Configuration conf = new DefaultConfiguration();
    Configuration conf = new Configuration("myconf");
    TimetableFitnessFunction fitnessFunction = new TimetableFitnessFunction();
    InitialConstraintChecker timetableConstraintChecker = new InitialConstraintChecker();

    // Creating genes
    Gene[] testGenes = new Gene[CHROMOSOME_SIZE];
    for (int i = 0; i < CHROMOSOME_SIZE; i++) {
      testGenes[i] =
          new GroupClassTeacherLessonTimeSG(
              conf,
              new Gene[] {
                new GroupGene(conf, 1),
                new ClassGene(conf, 1),
                new TeacherGene(conf, 1),
                new LessonGene(conf, 1),
                new TimeGene(conf, 1)
              });
    }
    System.out.println("==================================");
    // Creating chromosome
    Chromosome testChromosome;
    testChromosome = new Chromosome(conf, testGenes);
    testChromosome.setConstraintChecker(timetableConstraintChecker);
    // Setup configuration
    conf.setSampleChromosome(testChromosome);
    conf.setPopulationSize(POPULATION_SIZE);
    conf.setFitnessFunction(fitnessFunction); // add fitness function

    BestChromosomesSelector myBestChromosomesSelector = new BestChromosomesSelector(conf);
    conf.addNaturalSelector(myBestChromosomesSelector, false);

    conf.setRandomGenerator(new StockRandomGenerator());
    conf.setEventManager(new EventManager());
    conf.setFitnessEvaluator(new DefaultFitnessEvaluator());

    CrossoverOperator myCrossoverOperator = new CrossoverOperator(conf);
    conf.addGeneticOperator(myCrossoverOperator);

    TimetableMutationOperator myMutationOperator = new TimetableMutationOperator(conf);
    conf.addGeneticOperator(myMutationOperator);

    conf.setKeepPopulationSizeConstant(false);

    // Creating genotype
    //        Population pop = new Population(conf, testChromosome);
    //        Genotype population = new Genotype(conf, pop);
    Genotype population = Genotype.randomInitialGenotype(conf);

    System.out.println("Our Chromosome: \n " + testChromosome.getConfiguration().toString());

    System.out.println("------------evolution-----------------------------");

    // Begin evolution
    Calendar cal = Calendar.getInstance();
    start_t = cal.getTimeInMillis();
    for (int i = 0; i < MAX_EVOLUTIONS; i++) {
      System.out.println(
          "generation#: " + i + " population size:" + (Integer) population.getPopulation().size());
      if (population.getFittestChromosome().getFitnessValue() >= THRESHOLD) break;
      population.evolve();
    }
    cal = Calendar.getInstance();
    finish_t = cal.getTimeInMillis();

    System.out.println("--------------end of evolution--------------------");
    Chromosome fittestChromosome = (Chromosome) population.getFittestChromosome();
    System.out.println(
        "-------------The best chromosome---fitness="
            + fittestChromosome.getFitnessValue()
            + "---");
    System.out.println("                Group Class Time");
    for (int i = 0; i < CHROMOSOME_SIZE; i++) {
      GroupClassTeacherLessonTimeSG s =
          (GroupClassTeacherLessonTimeSG) fittestChromosome.getGene(i);
      System.out.println(
          "Gene "
              + i
              + " contains: "
              + (Integer) s.geneAt(GROUP).getAllele()
              + " "
              + (Integer) s.geneAt(CLASS).getAllele()
              + " "
              + (Integer) s.geneAt(TEACHER).getAllele()
              + " "
              + (Integer) s.geneAt(LESSON).getAllele()
              + " "
              + (Integer) s.geneAt(TIME).getAllele());
      // GroupGene gg = (GroupGene)s.geneAt(GROUP);
      // System.out.println("gg's idGroup"+gg.getAllele()+" gg.getGroupSize()"+ gg.getGroupSize() );
    }

    System.out.println("Elapsed time:" + (double) (finish_t - start_t) / 1000 + "s");

    // Display the best solution

    OutputData od = new OutputData();
    od.printToConsole(fittestChromosome);

    // Write population to the disk
    try {
      od.printToFile(population, GENOTYPE_FILENAME, BEST_CHROMOSOME_FILENAME);
    } catch (IOException e) {
      System.out.println("IOException raised! " + e.getMessage());
    }
  }