public override 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++)
{
// Grab the next individual out of the population
IGenome g = pop.GenomeList[i];
if (g.EvaluationCount != 0)
{
continue;
}
// The current genome is a CPPN genome, not a network genome
// So, decode the CPPN genome into a CPPN, use the CPPN to generate an ANN,
// then run the networkEvaluator on the ANN
INetwork cppn = g.Decode(activationFn);
if (cppn == null)
{ // Future genomes may not decode - handle the possibility.
g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
//BehaviorType behavior;
//INetwork network = Chromaria.Simulator.controllerSubstrate.generateGenome(cppn).Decode(activationFn);
g.Fitness = Math.Max(0.0f, EvolutionAlgorithm.MIN_GENOME_FITNESS);
//if (Chromaria.Simulator.plantingWasValid)
//Chromaria.Simulator.successfulPlanterGenome = g;
g.RealFitness = g.Fitness;
}
// Reset these genome level statistics.
g.TotalFitness = g.Fitness;
g.EvaluationCount = 1;
// Update master evaluation counter.
evaluationCount++;
// Close the XML tag for this individual
//Chromaria.Simulator.xmlWriter.WriteEndElement();
}
if (ea.NeatParameters.noveltySearch)
{
if (ea.NeatParameters.noveltySearch && ea.noveltyInitialized)
{
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();
}
}
}
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();
}
}
}
