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;
}
private void read_problem() throws IOException {
BufferedReader fp = new BufferedReader(new FileReader(input_file_name));
Vector<Double> vy = new Vector<Double>();
Vector<svm_node[]> vx = new Vector<svm_node[]>();
int max_index = 0;
while (true) {
String line = fp.readLine();
if (line == null) break;
StringTokenizer st = new StringTokenizer(line, " \t\n\r\f:");
vy.addElement(atof(st.nextToken()));
int m = st.countTokens() / 2;
svm_node[] x = new svm_node[m];
for (int j = 0; j < m; j++) {
x[j] = new svm_node();
x[j].index = atoi(st.nextToken());
x[j].value = atof(st.nextToken());
}
if (m > 0) max_index = Math.max(max_index, x[m - 1].index);
vx.addElement(x);
}
prob = new svm_problem();
prob.l = vy.size();
prob.x = new svm_node[prob.l][];
for (int i = 0; i < prob.l; i++) prob.x[i] = vx.elementAt(i);
prob.y = new double[prob.l];
for (int i = 0; i < prob.l; i++) prob.y[i] = vy.elementAt(i);
if (param.gamma == 0 && max_index > 0) param.gamma = 1.0 / max_index;
fp.close();
}
public void eval(
final EvolutionState state,
final int thread,
final GPData input,
final ADFStack stack,
final GPIndividual individual,
final Problem problem) {
RegressionData rd = ((RegressionData) (input));
children[0].eval(state, thread, input, stack, individual, problem);
rd.x = Math.tan(rd.x);
}