public void evaluate(
      final EvolutionState state,
      final Individual ind,
      final int subpopulation,
      final int threadnum) {
    if (!ind.evaluated) // don't bother reevaluating
    {
      // trees[0].child is the root
      double score = fitness(((GPIndividual) ind).trees[0].child, state);

      SimpleFitness f = ((SimpleFitness) ind.fitness);
      f.setFitness(state, score, false);
      ind.evaluated = true;
    }
  }
Example #2
0
  public void evaluate(
      final EvolutionState state,
      final Individual ind,
      final int subpopulation,
      final int threadnum) {
    float accuracy = (float) 0.0;
    int i;
    double total_correct = 0;

    if (!ind.evaluated) // don't bother reevaluating
    {
      SVM_GP.setInd(ind);
      SVM_GP.setInput(input);
      SVM_GP.setProblem(this);
      SVM_GP.setStack(stack);
      SVM_GP.setState(state);
      SVM_GP.setSubpopulation(subpopulation);
      SVM_GP.setThreadnum(threadnum);

      double[] target = new double[prob.l];
      SVM_GP.svm_cross_validation(prob, param, nr_fold, target);

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

      //              result = Math.abs(expectedResult - input.x);

      for (i = 0; i < prob.l; i++) if (target[i] == prob.y[i]) ++total_correct;
      accuracy = (float) (total_correct / prob.l);
      System.out.print("Cross Validation Accuracy = " + 100.0 * accuracy + "%\n");
    }
    SimpleFitness f = ((SimpleFitness) ind.fitness);
    f.setFitness(state, accuracy, accuracy == 1.0);
    // the fitness better be KozaFitness!

    // KozaFitness f = ((KozaFitness)ind.fitness);
    // f.setStandardizedFitness(state,(float)accuracy);
    // f.hits = hits;
    ind.evaluated = true;
  }