Ejemplo n.º 1
0
 /**
  * Convenience method that returns a new Chromosome instance with its genes values (alleles)
  * randomized. Note that, if possible, this method will acquire a Chromosome instance from the
  * active ChromosomePool (if any) and then randomize its gene values before returning it. If a
  * Chromosome cannot be acquired from the pool, then a new instance will be constructed and its
  * gene values randomized before returning it.
  *
  * @param a_configuration the configuration to use
  * @return randomly initialized Chromosome
  * @throws InvalidConfigurationException if the given Configuration instance is invalid
  * @throws IllegalArgumentException if the given Configuration instance is null
  * @author Neil Rotstan
  * @author Klaus Meffert
  * @since 1.0
  */
 public static IChromosome randomInitialChromosome(Configuration a_configuration)
     throws InvalidConfigurationException {
   // Sanity check: make sure the given configuration isn't null.
   // -----------------------------------------------------------
   if (a_configuration == null) {
     throw new IllegalArgumentException("Configuration instance must not be null");
   }
   // Lock the configuration settings so that they can't be changed
   // from now on.
   // -------------------------------------------------------------
   a_configuration.lockSettings();
   // First see if we can get a Chromosome instance from the pool.
   // If we can, we'll randomize its gene values (alleles) and then
   // return it.
   // -------------------------------------------------------------
   IChromosomePool pool = a_configuration.getChromosomePool();
   if (pool != null) {
     IChromosome randomChromosome = pool.acquireChromosome();
     if (randomChromosome != null) {
       Gene[] genes = randomChromosome.getGenes();
       RandomGenerator generator = a_configuration.getRandomGenerator();
       for (int i = 0; i < genes.length; i++) {
         genes[i].setToRandomValue(generator);
         /** @todo what about Gene's energy? */
       }
       randomChromosome.setFitnessValueDirectly(FitnessFunction.NO_FITNESS_VALUE);
       return randomChromosome;
     }
   }
   // We weren't able to get a Chromosome from the pool, so we have to
   // construct a new instance and build it from scratch.
   // ------------------------------------------------------------------
   IChromosome sampleChromosome = a_configuration.getSampleChromosome();
   sampleChromosome.setFitnessValue(FitnessFunction.NO_FITNESS_VALUE);
   Gene[] sampleGenes = sampleChromosome.getGenes();
   Gene[] newGenes = new Gene[sampleGenes.length];
   RandomGenerator generator = a_configuration.getRandomGenerator();
   for (int i = 0; i < newGenes.length; i++) {
     // We use the newGene() method on each of the genes in the
     // sample Chromosome to generate our new Gene instances for
     // the Chromosome we're returning. This guarantees that the
     // new Genes are setup with all of the correct internal state
     // for the respective gene position they're going to inhabit.
     // -----------------------------------------------------------
     newGenes[i] = sampleGenes[i].newGene();
     // Set the gene's value (allele) to a random value.
     // ------------------------------------------------
     newGenes[i].setToRandomValue(generator);
     /** @todo what about Gene's energy? */
   }
   // Finally, construct the new chromosome with the new random
   // genes values and return it.
   // ---------------------------------------------------------
   return new Chromosome(a_configuration, newGenes);
 }
  /**
   * 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.");
  }
Ejemplo n.º 3
0
  /**
   * Evolves the population of chromosomes within a genotype. This will execute all of the genetic
   * operators added to the present active configuration and then invoke the natural selector to
   * choose which chromosomes will be included in the next generation population.
   *
   * @param a_pop the population to evolve
   * @param a_conf the configuration to use for evolution
   * @return evolved population
   * @author Klaus Meffert
   * @since 3.2
   */
  public Population evolve(Population a_pop, Configuration a_conf) {
    Population pop = a_pop;
    int originalPopSize = a_conf.getPopulationSize();
    boolean monitorActive = a_conf.getMonitor() != null;
    IChromosome fittest = null;
    // If first generation: Set age to one to allow genetic operations,
    // see CrossoverOperator for an illustration.
    // ----------------------------------------------------------------
    if (a_conf.getGenerationNr() == 0) {
      int size = pop.size();
      for (int i = 0; i < size; i++) {
        IChromosome chrom = pop.getChromosome(i);
        chrom.increaseAge();
      }
    } else {
      // Select fittest chromosome in case it should be preserved and we are
      // not in the very first generation.
      // -------------------------------------------------------------------
      if (a_conf.isPreserveFittestIndividual()) {
        /** @todo utilize jobs. In pop do also utilize jobs, especially for fitness computation */
        fittest = pop.determineFittestChromosome(0, pop.size() - 1);
      }
    }
    if (a_conf.getGenerationNr() > 0) {
      // Adjust population size to configured size (if wanted).
      // Theoretically, this should be done at the end of this method.
      // But for optimization issues it is not. If it is the last call to
      // evolve() then the resulting population possibly contains more
      // chromosomes than the wanted number. But this is no bad thing as
      // more alternatives mean better chances having a fit candidate.
      // If it is not the last call to evolve() then the next call will
      // ensure the correct population size by calling keepPopSizeConstant.
      // ------------------------------------------------------------------
      keepPopSizeConstant(pop, a_conf);
    }
    // Ensure fitness value of all chromosomes is udpated.
    // ---------------------------------------------------
    if (monitorActive) {
      // Monitor that fitness value of chromosomes is being updated.
      // -----------------------------------------------------------
      a_conf
          .getMonitor()
          .event(
              IEvolutionMonitor.MONITOR_EVENT_BEFORE_UPDATE_CHROMOSOMES1,
              a_conf.getGenerationNr(),
              new Object[] {pop});
    }
    updateChromosomes(pop, a_conf);
    if (monitorActive) {
      // Monitor that fitness value of chromosomes is being updated.
      // -----------------------------------------------------------
      a_conf
          .getMonitor()
          .event(
              IEvolutionMonitor.MONITOR_EVENT_AFTER_UPDATE_CHROMOSOMES1,
              a_conf.getGenerationNr(),
              new Object[] {pop});
    }
    // Apply certain NaturalSelectors before GeneticOperators will be executed.
    // ------------------------------------------------------------------------
    pop = applyNaturalSelectors(a_conf, pop, true);
    // Execute all of the Genetic Operators.
    // -------------------------------------
    applyGeneticOperators(a_conf, pop);
    // Reset fitness value of genetically operated chromosomes.
    // Normally, this should not be necessary as the Chromosome class
    // initializes each newly created chromosome with
    // FitnessFunction.NO_FITNESS_VALUE. But who knows which Chromosome
    // implementation is used...
    // ----------------------------------------------------------------
    int currentPopSize = pop.size();
    for (int i = originalPopSize; i < currentPopSize; i++) {
      IChromosome chrom = pop.getChromosome(i);
      chrom.setFitnessValueDirectly(FitnessFunction.NO_FITNESS_VALUE);
      // Mark chromosome as new-born.
      // ----------------------------
      chrom.resetAge();
      // Mark chromosome as being operated on.
      // -------------------------------------
      chrom.increaseOperatedOn();
    }
    // Increase age of all chromosomes which are not modified by genetic
    // operations.
    // -----------------------------------------------------------------
    int size = Math.min(originalPopSize, currentPopSize);
    for (int i = 0; i < size; i++) {
      IChromosome chrom = pop.getChromosome(i);
      chrom.increaseAge();
      // Mark chromosome as not being operated on.
      // -----------------------------------------
      chrom.resetOperatedOn();
    }
    // If a bulk fitness function has been provided, call it.
    // ------------------------------------------------------
    BulkFitnessFunction bulkFunction = a_conf.getBulkFitnessFunction();
    if (bulkFunction != null) {
      if (monitorActive) {
        // Monitor that bulk fitness will be called for evaluation.
        // --------------------------------------------------------
        a_conf
            .getMonitor()
            .event(
                IEvolutionMonitor.MONITOR_EVENT_BEFORE_BULK_EVAL,
                a_conf.getGenerationNr(),
                new Object[] {bulkFunction, pop});
      }
      /** @todo utilize jobs: bulk fitness function is not so important for a prototype! */
      bulkFunction.evaluate(pop);
      if (monitorActive) {
        // Monitor that bulk fitness has been called for evaluation.
        // ---------------------------------------------------------
        a_conf
            .getMonitor()
            .event(
                IEvolutionMonitor.MONITOR_EVENT_AFTER_BULK_EVAL,
                a_conf.getGenerationNr(),
                new Object[] {bulkFunction, pop});
      }
    }
    // Ensure fitness value of all chromosomes is udpated.
    // ---------------------------------------------------
    if (monitorActive) {
      // Monitor that fitness value of chromosomes is being updated.
      // -----------------------------------------------------------
      a_conf
          .getMonitor()
          .event(
              IEvolutionMonitor.MONITOR_EVENT_BEFORE_UPDATE_CHROMOSOMES2,
              a_conf.getGenerationNr(),
              new Object[] {pop});
    }
    updateChromosomes(pop, a_conf);
    if (monitorActive) {
      // Monitor that fitness value of chromosomes is being updated.
      // -----------------------------------------------------------
      a_conf
          .getMonitor()
          .event(
              IEvolutionMonitor.MONITOR_EVENT_AFTER_UPDATE_CHROMOSOMES2,
              a_conf.getGenerationNr(),
              new Object[] {pop});
    }
    // Apply certain NaturalSelectors after GeneticOperators have been applied.
    // ------------------------------------------------------------------------
    pop = applyNaturalSelectors(a_conf, pop, false);
    // Fill up population randomly if size dropped below specified percentage
    // of original size.
    // ----------------------------------------------------------------------
    if (a_conf.getMinimumPopSizePercent() > 0) {
      int sizeWanted = a_conf.getPopulationSize();
      int popSize;
      int minSize = (int) Math.round(sizeWanted * (double) a_conf.getMinimumPopSizePercent() / 100);
      popSize = pop.size();
      if (popSize < minSize) {
        IChromosome newChrom;
        IChromosome sampleChrom = a_conf.getSampleChromosome();
        Class sampleChromClass = sampleChrom.getClass();
        IInitializer chromIniter =
            a_conf.getJGAPFactory().getInitializerFor(sampleChrom, sampleChromClass);
        while (pop.size() < minSize) {
          try {
            /**
             * @todo utilize jobs as initialization may be time-consuming as invalid combinations
             *     may have to be filtered out
             */
            newChrom = (IChromosome) chromIniter.perform(sampleChrom, sampleChromClass, null);
            if (monitorActive) {
              // Monitor that fitness value of chromosomes is being updated.
              // -----------------------------------------------------------
              a_conf
                  .getMonitor()
                  .event(
                      IEvolutionMonitor.MONITOR_EVENT_BEFORE_ADD_CHROMOSOME,
                      a_conf.getGenerationNr(),
                      new Object[] {pop, newChrom});
            }
            pop.addChromosome(newChrom);
          } catch (Exception ex) {
            throw new RuntimeException(ex);
          }
        }
      }
    }
    IChromosome newFittest = reAddFittest(pop, fittest);
    if (monitorActive && newFittest != null) {
      // Monitor that fitness value of chromosomes is being updated.
      // -----------------------------------------------------------
      a_conf
          .getMonitor()
          .event(
              IEvolutionMonitor.MONITOR_EVENT_READD_FITTEST,
              a_conf.getGenerationNr(),
              new Object[] {pop, fittest});
    }

    // Increase number of generations.
    // -------------------------------
    a_conf.incrementGenerationNr();
    // Fire an event to indicate we've performed an evolution.
    // -------------------------------------------------------
    m_lastPop = pop;
    m_lastConf = a_conf;
    a_conf
        .getEventManager()
        .fireGeneticEvent(new GeneticEvent(GeneticEvent.GENOTYPE_EVOLVED_EVENT, this));
    return pop;
  }