Exemplo n.º 1
0
  /** Applies a roulette wheel selection */
  public void selection() {
    RuleSet temp[];
    double probability[] = new double[long_poblacion];
    double total;
    double prob;
    int sel;

    temp = new RuleSet[long_poblacion];
    // sort the poblation in order of fitness
    Arrays.sort(poblacion, Collections.reverseOrder());

    probability[0] = poblacion[0].getFitness();
    for (int i = 1; i < long_poblacion; i++) {
      probability[i] = probability[i - 1] + poblacion[i].getFitness();
    }
    total = probability[long_poblacion - 1];
    for (int i = 0; i < long_poblacion; i++) {
      probability[i] /= total;
    }

    for (int i = 0; i < long_poblacion; i++) {
      prob = Randomize.Rand();
      sel = -1;
      for (int j = 0; j < long_poblacion && sel == -1; j++) {
        if (probability[j] > prob) sel = j;
      }
      temp[i] = new RuleSet(poblacion[sel]);
    }

    previousPob = poblacion;
    poblacion = temp;
  }
Exemplo n.º 2
0
  /**
   * It runs the Single-attribute Evolution Module (SEM) algorithm to obtain a rule set of ONE
   * attribute
   */
  public void run() {
    boolean endCondition = false;
    int gen = 0;
    int stagnation = 0;

    evaluate();
    while (!endCondition) {
      tournament_selection();

      crossOver();

      mutate();
      elitism();
      evaluate();

      Arrays.sort(poblacion, Collections.reverseOrder());
      gen++;
      if (bestCR != poblacion[0].getFitness()) stagnation = 0;
      else stagnation++;
      if (gen > generationLimit || stagnation > stagnationLimit || poblacion[0].getFitness() == 1.0)
        endCondition = true;
      bestCR = poblacion[0].getFitness();
    }
  }