示例#1
0
  public void describe(
      final EvolutionState state,
      final Individual ind,
      final int subpopulation,
      final int threadnum,
      final int log) {
    if (ca == null) ca = new CA(CA_WIDTH, NEIGHBORHOOD);

    int[] trial = new int[CA_WIDTH];

    MajorityData input = (MajorityData) (this.input);

    // extract the rule
    ((GPIndividual) ind)
        .trees[0].child.eval(state, threadnum, input, stack, ((GPIndividual) ind), this);

    int[] rule = ca.getRule();
    for (int i = 0; i < 64; i++) rule[i] = (int) (((input.data0) >> i) & 0x1);
    for (int i = 64; i < 128; i++) rule[i] = (int) (((input.data1) >> (i - 64)) & 0x1);
    ca.setRule(rule); // for good measure though it doesn't matter

    // print rule
    String s = "Rule: ";
    for (int i = 0; i < rule.length; i++) s += rule[i];
    state.output.println(s, log);

    double sum = 0;
    for (int i = 0; i < NUM_TESTS; i++) {
      // set up and run the CA
      int result = makeTrial(state, threadnum, trial, RANDOM) ? 1 : 0;
      ca.setVals(trial);
      ca.step(STEPS, true);

      // extract the fitness
      if (all(ca.getVals(), result)) sum++;
    }

    density = (sum / NUM_TESTS);
    state.output.println("Generalization Accuracy: " + density, 1); // stderr
    state.output.println("Generalization Accuracy: " + density, log);
  }
示例#2
0
  public void evaluate(
      final EvolutionState state,
      final Individual ind,
      final int subpopulation,
      final int threadnum) {
    if (ca == null) ca = new CA(CA_WIDTH, NEIGHBORHOOD);

    // we always reevaluate
    // if (!ind.evaluated)  // don't bother reevaluating
    {
      MajorityData input = (MajorityData) (this.input);

      int sum = 0;

      // extract the rule
      ((GPIndividual) ind)
          .trees[0].child.eval(state, threadnum, input, stack, ((GPIndividual) ind), this);

      int[] rule = ca.getRule();
      for (int i = 0; i < 64; i++) rule[i] = (int) (((input.data0) >> i) & 0x1);
      for (int i = 64; i < 128; i++) rule[i] = (int) (((input.data1) >> (i - 64)) & 0x1);
      ca.setRule(rule); // for good measure though it doesn't matter

      for (int i = 0; i < NUM_TRIALS; i++) {
        // set up and run the CA
        ca.setVals(trials[i]);
        ca.step(STEPS, true);

        // extract the fitness
        if (all(ca.getVals(), majorities[i])) sum++;
      }

      SimpleFitness f = ((SimpleFitness) ind.fitness);
      f.setFitness(state, sum / (double) NUM_TRIALS, (sum == NUM_TRIALS));
      ind.evaluated = true;
    }
  }