public void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
int count = pop.GenomeList.Count;
evalPack e;
IGenome g;
int i;
for (i = 0; i < count; i++)
{
//Console.WriteLine(i);
sem.WaitOne();
g = pop.GenomeList[i];
e = new evalPack(networkEvaluator, activationFn, g, i % HyperNEATParameters.numThreads,(int)ea.Generation);
ThreadPool.QueueUserWorkItem(new WaitCallback(evalNet), e);
// Update master evaluation counter.
evaluationCount++;
/*if(printFinalPositions)
file.WriteLine(g.Behavior.behaviorList[0].ToString() + ", " + g.Behavior.behaviorList[1].ToString());//*/
}
//Console.WriteLine("waiting for last threads..");
for (int j = 0; j < HyperNEATParameters.numThreads; j++)
{
sem.WaitOne();
// Console.WriteLine("waiting");
}
for (int j = 0; j < HyperNEATParameters.numThreads; j++)
{
//Console.WriteLine("releasing");
sem.Release();
}
//Console.WriteLine("generation done...");
//calulate novelty scores...
if(ea.NeatParameters.noveltySearch)
{
if(ea.NeatParameters.noveltySearch)
{
ea.CalculateNovelty();
}
}
}
public virtual void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
// Evaluate in single-file each genome within the population.
// Only evaluate new genomes (those with EvaluationCount==0).
int count = pop.GenomeList.Count;
for(int i=0; i<count; i++)
{
IGenome g = pop.GenomeList[i];
if(g.EvaluationCount!=0)
continue;
INetwork network = g.Decode(activationFn);
if(network==null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
BehaviorType behavior;
g.Fitness = Math.Max(networkEvaluator.EvaluateNetwork(network,out behavior), EvolutionAlgorithm.MIN_GENOME_FITNESS);
g.RealFitness = g.Fitness;
g.Behavior = behavior;
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
}
if(ea.NeatParameters.noveltySearch)
{
if(ea.NeatParameters.noveltySearch && ea.noveltyInitialized)
{
ea.CalculateNovelty();
}
}
}