示例#1
0
  /**
   * Mutates the classifier. It mutates the action and the condition.
   *
   * @param envState is the current environtment state. It is necessary for the niched mutation.
   * @return a boolean indicating if the action has been mutated.
   */
  public boolean mutate(double[] envState) {
    int i;

    // First, the condition is mutated
    for (i = 0; i < rep.length; i++) {
      rep[i].mutate(envState[i]);
    }

    // Second, the action is mutated
    int act = 0;
    if (Config.rand() < Config.pM) {
      do {
        act = (int) (Config.rand() * (double) Config.numberOfActions);
      } while (action == act);
      action = act;
      return true;
    }
    return false;
  } // end mutate
示例#2
0
  /**
   * Applies crossover. It generates two children.
   *
   * @param parent1 is the first parent.
   * @param parent2 is the second parent
   * @param child1 is the first child
   * @param child2 is the second child.
   */
  public void makeCrossover(
      Classifier parent1, Classifier parent2, Classifier child1, Classifier child2) {
    int i = 0;

    // cross1 is a number between [0.. clLength-1]
    int cross1 = (int) (Config.rand() * (double) Config.clLength);

    // cross2 is a number between [1..clLenght].
    int cross2 = (int) (Config.rand() * (double) Config.clLength) + 1;

    if (cross1 > cross2) {
      int aux = cross2;
      cross2 = cross1;
      cross1 = aux;
      // In the else-if condition is not necessary to check if (cross2<clLength)
      // to increment the point, because cross1 [0..length-1]
    } else if (cross1 == cross2) cross2++;

    // All the intervals (real representation) or genes (ternary representation) that
    // are not in the cross point are crossed.
    if (!Config.ternaryRep) {
      for (i = cross1 + 1; i < cross2 - 1; i++) {
        child2.setAllele(i, parent1);
        child1.setAllele(i, parent2);
      }

      // Now we have to cross the border allele
      child1.crossAllele(cross1, parent1, parent2);
      child1.crossAllele(cross2 - 1, parent2, parent1);
      child2.crossAllele(cross1, parent2, parent1);
      child2.crossAllele(cross2 - 1, parent1, parent2);
    } else {
      for (i = cross1; i < cross2 - 1; i++) {
        child2.setAllele(i, parent1);
        child1.setAllele(i, parent2);
      }
    }
  } // end makeCrossover
示例#3
0
 /**
  * It crosses a real allele within two parents. If the representation is a ternary representation,
  * a crossover within intervals is not possible because there is only one gene in each position.
  * So, in this case, the gene of the second parent will be copied. In case of being a real
  * representation, a random number is generated to decide where to cross the interval. It it's
  * crossed within the inteval, the crossAllele method will do it.
  *
  * @param parent1 is the first parent classifier.
  * @param parent2 is the second parent classifier.
  */
 public void crossAllele(int i, Classifier parent1, Classifier parent2) {
   if (Config.ternaryRep) {
     rep[i].setAllele(parent2.rep[i]);
   } else {
     if (Config.typeOfAttributes[i].equals("ternary")) {
       rep[i].setAllele(parent2.rep[i]);
     } else {
       if (Config.rand() < 0.5) rep[i].setAllele(parent2.rep[i]);
       else { // The alleles has to be crossed
         rep[i].setAllele(parent1.rep[i].getLowerAllele(), parent2.rep[i].getUpperAllele());
         rep[i].verifyInterval();
       }
     }
   }
 } // end crossAllele
示例#4
0
  /**
   * It constructs a classifier with the condition and the action specified. It's used by the
   * covering operator.
   *
   * <p>
   *
   * @param condition is the environtment state
   * @param action is the action chosen
   * @param size is the size of the set.
   * @param tStamp is the time
   */
  public Classifier(double[] envState, int classOfExample, int size, int tStamp) {
    int i = 0;
    rep = new Attribute[Config.clLength];

    if (Config.ternaryRep) {
      for (i = 0; i < rep.length; i++) rep[i] = new TernaryRep(envState[i]);
    } else {
      for (i = 0; i < rep.length; i++) {
        if (Config.typeOfAttributes[i].equals("ternary")) rep[i] = new TernaryRep(envState[i]);
        else {
          if (envState[i] == Config.unknownValue) rep[i] = new RealRep(Config.rand());
          else rep[i] = new RealRep(envState[i]);
        }
      }
    }
    action = classOfExample;

    parameters = new Parameters(tStamp, size, getGenerality());
  } // end Classifier