public Individual run(
EvolutionState state,
SimpleProblemForm p,
Individual x,
int maxIter,
ClusWrapperNonStatic objectClus)
throws Exception { // talvez passe um individuo já com o genoma e score
Random rand = new Random();
// IntegerVectorIndividual mutatedX = (IntegerVectorIndividual) deepClone(x);
IntegerVectorIndividual mutatedX = (IntegerVectorIndividual) x.clone();
mutatedX.evaluated = false;
double alpha = 0.95; // cálculo do alpha, percentual de diminuição da temperatura
double t_final = 1;
double t0 = Math.pow(alpha, maxIter + Math.log(t_final)); // estimar a temperatura inicial
double temp_atual = t0; // tf = temperatura da vez
int iter = 0;
maxIter *= 2;
while (iter < maxIter) {
int[] genome = mutatedX.genome;
genome = mutate(genome, 1 - iter / maxIter); // mutar o vetor
mutatedX.setGenome(genome);
((ec.Problem) p).prepareToEvaluate(state, 0);
System.out.print("Mutated Genoma: " + mutatedX.genome[0]);
for (int i = 0; i < mutatedX.genomeLength(); i++) System.out.print("," + mutatedX.genome[i]);
System.out.println();
p.evaluate(state, mutatedX, 0, 0, objectClus);
((ec.Problem) p).finishEvaluating(state, 0);
double newF = mutatedX.fitness.fitness();
// adequar para minimizar o fitness
float delta = (float) (x.fitness.fitness() - newF);
if (delta < 0 || rand.nextDouble() < Math.exp(-(delta) / temp_atual)) {
((IntegerVectorIndividual) x).setGenome(mutatedX.getGenome());
x.evaluated = false;
((ec.Problem) p).prepareToEvaluate(state, 0);
p.evaluate(state, x, 0, 0, objectClus);
((ec.Problem) p).finishEvaluating(state, 0);
}
iter += 1;
temp_atual = temp_atual * alpha;
}
return x;
}
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;
}
}