public static void evalNet(Object input)
{
evalPack e = (evalPack)input;
if (e.g == null) //|| e.g.EvaluationCount != 0)
{
sem.Release();
return;
}
sem2.WaitOne();
INetwork network = e.g.Decode(e.Activation);
sem2.Release();
if (network == null)
{ // Future genomes may not decode - handle the possibility.
e.g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
}
else
{
e.g.Fitness = Math.Max(e.NetworkEvaluator.threadSafeEvaluateNetwork(network, sem2), EvolutionAlgorithm.MIN_GENOME_FITNESS);
}
// Reset these genome level statistics.
e.g.TotalFitness += e.g.Fitness;
e.g.EvaluationCount += 1;
sem.Release();
}
public static void evalNet(Object input)
{
evalPack e = (evalPack)input;
if (e.g == null || (!HyperNEATParameters.reevaluateEveryGeneration && e.g.EvaluationCount != 0))
{
sem.Release();
return;
}
sem2.WaitOne();
INetwork network = e.g.Decode(e.Activation);
sem2.Release();
if (network == null)
{ // Future genomes may not decode - handle the possibility.
e.g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
e.g.RealFitness = e.g.Fitness;
}
else
{
BehaviorType behavior;
e.g.Fitness = Math.Max(e.NetworkEvaluator.threadSafeEvaluateNetwork(network, sem2, out behavior, e.ThreadNumber), EvolutionAlgorithm.MIN_GENOME_FITNESS);
e.g.Behavior = behavior;
e.g.RealFitness = e.g.Fitness;
}
// Reset these genome level statistics.
e.g.TotalFitness += e.g.Fitness;
e.g.EvaluationCount += 1;
sem.Release();
}
public void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
int count = pop.GenomeList.Count;
evalPack e;
IGenome g;
int i;
for (i = 0; i < count; i++)
{
sem.WaitOne();
g = pop.GenomeList[i];
e = new evalPack(networkEvaluator, activationFn, g);
ThreadPool.QueueUserWorkItem(new WaitCallback(evalNet), e);
// Update master evaluation counter.
evaluationCount++;
}
for (int j = 0; j < HyperNEATParameters.numThreads; j++)
{
sem.WaitOne();
}
for (int j = 0; j < HyperNEATParameters.numThreads; j++)
{
sem.Release();
}
}
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 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 static void evalNet(Object input)
{
evalPack e = (evalPack)input;
if (e.g == null || (!HyperNEATParameters.reevaluateEveryGeneration && e.g.EvaluationCount != 0))
{
sem.Release();
return;
}
sem2.WaitOne();
// Schrum: Debugging
/*
* XmlDocument doc = new XmlDocument();
* XmlGenomeWriterStatic.Write(doc, (SharpNeatLib.NeatGenome.NeatGenome)e.g);
* FileInfo oFileInfo = new FileInfo("MostRecentlySaved.xml");
* doc.Save(oFileInfo.FullName);
*/
INetwork network = e.g.Decode(e.Activation);
sem2.Release();
if (network == null)
{ // Future genomes may not decode - handle the possibility.
e.g.Fitness = EvolutionAlgorithm.MIN_GENOME_FITNESS;
e.g.RealFitness = e.g.Fitness;
}
else
{
BehaviorType behavior;
e.g.Fitness = Math.Max(e.NetworkEvaluator.threadSafeEvaluateNetwork(network, sem2, out behavior, e.ThreadNumber), EvolutionAlgorithm.MIN_GENOME_FITNESS);
e.g.Behavior = behavior;
e.g.RealFitness = e.g.Fitness;
}
// Reset these genome level statistics.
e.g.TotalFitness += e.g.Fitness;
e.g.EvaluationCount += 1;
sem.Release();
}
public void EvaluatePopulation(Population pop, EvolutionAlgorithm ea)
{
int count = pop.GenomeList.Count;
evalPack e;
IGenome g;
int i;
for (i = 0; i < count; i++)
{
sem.WaitOne();
g = pop.GenomeList[i];
e = new evalPack(networkEvaluator, activationFn, g);
ThreadPool.QueueUserWorkItem(new WaitCallback(evalNet), e);
// Update master evaluation counter.
evaluationCount++;
}
for (int j = 0; j < HyperNEATParameters.numThreads; j++)
{
sem.WaitOne();
}
for (int j = 0; j < HyperNEATParameters.numThreads; j++)
{
sem.Release();
}
}
