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);
}
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;
}
}
