public void Evaluate(IEvolutionState state,
Individual ind,
int subpopulation,
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;
}
}
public void Evaluate(IEvolutionState state,
Individual ind,
int subpopulation,
int threadnum)
{
if (_ca == null)
{
_ca = new CA(CA_WIDTH, NEIGHBORHOOD);
}
// we always reevaluate
//if (!ind.evaluated) // don't bother reevaluating
{
int sum = 0;
bool[] genome = ((BitVectorIndividual)ind).genome;
// extract the rule
int[] rule = _ca.GetRule();
for (int i = 0; i < 128; i++)
{
rule[i] = (genome[i] ? 1 : 0);
}
_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, false);
ind.Evaluated = true;
}
}
