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;
}
}
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;
}
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;
}
}