Examples of org.encog.ml.genetic.genome.Genome

org.encog.ml.genetic.genome.Genome
A genome is the basic blueprint for creating an organism in Encog. A genome is made up of one or more chromosomes, which are in turn made up of genes.

      calculateScore(genome);
    }


    getPopulation().sort();


    final Genome genome = getPopulation().getBest();
    final double currentBest = genome.getScore();


    if (getComparator().isBetterThan(currentBest, this.bestEverScore)) {
      this.bestEverScore = currentBest;
      this.bestEverNetwork = ((NEATNetwork) genome.getOrganism());
    }


    this.bestEverScore = getComparator().bestScore(getError(),
        this.bestEverScore);
  }

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    }


    // set the species leader links
    for (Species species : leaderMap.keySet()) {
      int leaderID = leaderMap.get(species);
      Genome leader = genomeMap.get(leaderID);
      species.setLeader(leader);
      ((BasicSpecies)species).setPopulation(result);
    }


    return result;

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  public class NeuralGeneticAlgorithmHelper extends BasicGeneticAlgorithm {
    /**
     * @return The error from the last iteration.
     */
    public final double getError() {
      final Genome genome = getPopulation().getBest();
      return genome.getScore();
    }

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     * Get the current best neural network.
     * 
     * @return The current best neural network.
     */
    public final MLMethod getMethod() {
      final Genome genome = getPopulation().getBest();
      return (BasicNetwork) genome.getOrganism();
    }

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   * 
   * @return The parent.
   */
  @Override
  public final Genome chooseParent() {
    Genome baby;


    // If there is a single member, then choose that one.
    if (this.members.size() == 1) {
      baby = this.members.get(0);
    } else {

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    final TaskGroup group = EngineConcurrency.getInstance()
        .createTaskGroup();


    // mate and form the next generation
    for (int i = 0; i < countToMate; i++) {
      final Genome mother = getPopulation().getGenomes().get(i);
      final int fatherInt = (int) (Math.random() * matingPopulationSize);
      final Genome father = getPopulation().getGenomes().get(fatherInt);
      final Genome child1 = getPopulation().getGenomes().get(
          offspringIndex);
      final Genome child2 = getPopulation().getGenomes().get(
          offspringIndex + 1);


      final MateWorker worker = new MateWorker(mother, father, child1,
          child2);

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      String prefix = dialog.getPrefix().getValue();
      int count = dialog.getGenomesToExtract().getValue();
      
      for(int i=0;i<count;i++)
      {
        Genome genome = this.population.getGenomes().get(i);
        genome.decode();
        NEATNetwork network = (NEATNetwork)genome.getOrganism();
        String name = FileUtil.forceExtension( prefix + i, "eg" );
        File path = new File(EncogWorkBench.getInstance().getProjectDirectory(),name);
        EncogWorkBench.getInstance().save(path, network);
        
      }

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    String type = "Unknown";
    
    if( population.getGenomes().size()>0 )
    {
      Genome genome = population.getGenomes().get(0);
      if( genome instanceof NEATGenome )
      {
        type = "Neat";
      }
      else if( genome instanceof NeuralGenome )

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Related Classes of org.encog.ml.genetic.genome.Genome

org.encog.ml.genetic.BasicGeneticAlgorithm

org.encog.ml.genetic.species.BasicSpecies

org.encog.neural.neat.PersistNEATPopulation

org.encog.neural.neat.training.NEATTraining

org.encog.neural.networks.training.genetic.NeuralGeneticAlgorithm$NeuralGeneticAlgorithmHelper

org.encog.workbench.tabs.population.neat.NEATPopulationInfo

org.encog.workbench.tabs.population.neat.NEATPopulationTab

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