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

org.encog.ml.genetic.genome.Chromosome
Implements a chromosome to genetic algorithm. This is an abstract class. Other classes are provided in this book that use this base class to train neural networks or provide an answer to the traveling salesman problem. Chromosomes are made up of genes. Genomes in this genetic algorithm consist of one or more chromosomes.

   */
  public NeuralGenome(
      final BasicNetwork network) {
    setOrganism(network);


    this.networkChromosome = new Chromosome();


    // create an array of "double genes"
    final int size = network.getStructure().calculateSize();
    for (int i = 0; i < size; i++) {
      final Gene gene = new DoubleGene();

View Full Code Here

      else {
        best = NEATParent.Dad;
      }
    }


    final Chromosome babyNeurons = new Chromosome();
    final Chromosome babyGenes = new Chromosome();


    final List<Long> vecNeurons = new ArrayList<Long>();


    int curMom = 0;
    int curDad = 0;


    NEATLinkGene momGene;
    NEATLinkGene dadGene;


    NEATLinkGene selectedGene = null;


    while ((curMom < mom.getNumGenes()) || (curDad < dad.getNumGenes())) {


      if (curMom < mom.getNumGenes()) {
        momGene = (NEATLinkGene) mom.getLinks().get(curMom);
      } else {
        momGene = null;
      }


      if (curDad < dad.getNumGenes()) {
        dadGene = (NEATLinkGene) dad.getLinks().get(curDad);
      } else {
        dadGene = null;
      }


      if ((momGene == null) && (dadGene != null)) {
        if (best == NEATParent.Dad) {
          selectedGene = dadGene;
        }
        curDad++;
      } else if ((dadGene == null) && (momGene != null)) {
        if (best == NEATParent.Mom) {
          selectedGene = momGene;
        }
        curMom++;
      } else if (momGene.getInnovationId() < dadGene.getInnovationId()) {
        if (best == NEATParent.Mom) {
          selectedGene = momGene;
        }
        curMom++;
      } else if (dadGene.getInnovationId() < momGene.getInnovationId()) {
        if (best == NEATParent.Dad) {
          selectedGene = dadGene;
        }
        curDad++;
      } else if (dadGene.getInnovationId() == momGene.getInnovationId()) {
        if (Math.random() < 0.5f) {
          selectedGene = momGene;
        }


        else {
          selectedGene = dadGene;
        }
        curMom++;
        curDad++;


      }


      if (babyGenes.size() == 0) {
        babyGenes.add(selectedGene);
      }


      else {
        if (((NEATLinkGene) babyGenes.get(babyGenes.size() - 1))
            .getInnovationId() != selectedGene.getInnovationId()) {
          babyGenes.add(selectedGene);
        }
      }


      // Check if we already have the nodes referred to in SelectedGene.
      // If not, they need to be added.

View Full Code Here

        NEATGenome lastGenome = null;
        for (String line : section.getLines()) {
          List<String> cols = EncogFileSection.splitColumns(line);
          if (cols.get(0).equalsIgnoreCase("g") ) {
            lastGenome = new NEATGenome();
            lastGenome.setNeuronsChromosome(new Chromosome());
            lastGenome.setLinksChromosome(new Chromosome());
            lastGenome.getChromosomes().add(
                lastGenome.getNeuronsChromosome());
            lastGenome.getChromosomes().add(
                lastGenome.getLinksChromosome());
            lastGenome.setGenomeID(Integer.parseInt(cols.get(1)));

View Full Code Here

   *
   * @param other
   *            The other genome.
   */
  public NEATGenome(final NEATGenome other) {
    this.neuronsChromosome = new Chromosome();
    this.linksChromosome = new Chromosome();
    this.setGeneticAlgorithm(other.getGeneticAlgorithm());


    getChromosomes().add(this.neuronsChromosome);
    getChromosomes().add(this.linksChromosome);

View Full Code Here

    this.outputCount = outputCount;
    setAmountToSpawn(0);
    this.speciesID = 0;


    final double inputRowSlice = 0.8 / (inputCount);
    this.neuronsChromosome = new Chromosome();
    this.linksChromosome = new Chromosome();


    getChromosomes().add(this.neuronsChromosome);
    getChromosomes().add(this.linksChromosome);


    for (int i = 0; i < inputCount; i++) {

View Full Code Here

        }
        organism[i + 1] = icandidate;
      }
    }
    
    this.pathChromosome = new Chromosome();
    this.getChromosomes().add(this.pathChromosome);
    
    for(int i=0;i<organism.length;i++)
    {
      IntegerGene gene = new IntegerGene();

View Full Code Here


  }


  @Override
  public void decode() {
    Chromosome chromosome = this.getChromosomes().get(0);
    int[] organism = new int[chromosome.size()];
    
    for(int i=0;i<chromosome.size();i++)
    {
      IntegerGene gene = (IntegerGene)chromosome.get(i);
      organism[i] = gene.getValue();
    }
    
    setOrganism(organism);
  }

View Full Code Here

    setOrganism(organism);
  }


  @Override
  public void encode() {
    Chromosome chromosome = this.getChromosomes().get(0);
    
    int[] organism = (int[])getOrganism();


    for(int i=0;i<chromosome.size();i++)
    {
      IntegerGene gene = (IntegerGene)chromosome.get(i);
      gene.setValue(organism[i]);
    }
  }

View Full Code Here

  private Chromosome chromosome;
  private int length;
  
  public PlayerGenome(GeneticAlgorithm geneticAlgorithm, Player player) {    
    this.player = player;
    this.chromosome = new Chromosome();
    setOrganism(this.player);
    
    this.length = player.getRules().length;
    
    for(int i=0;i<length;i++)

View Full Code Here

TOP

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

org.encog.examples.nonlinear.basicstrategy.PlayerGenome

org.encog.examples.nonlinear.tsp.genetic.TSPGenome

org.encog.neural.neat.PersistNEATPopulation

org.encog.neural.neat.training.NEATGenome

org.encog.neural.neat.training.NEATTraining

org.encog.neural.networks.training.genetic.NeuralGenome

All source code are property of their respective owners. Java is a trademark of Sun Microsystems, Inc and owned by ORACLE Inc. Contact coftware#gmail.com.