private void EvolutionaryThread()
{
m_exp = CreateExperiment();
var idgen = new IdGenerator();
m_evoAlg = new EvolutionAlgorithm(
new Population(idgen,
GenomeFactory.CreateGenomeList(m_exp.DefaultNeatParameters, idgen,
m_exp.InputNeuronCount, m_exp.OutputNeuronCount,
m_exp.DefaultNeatParameters.pInitialPopulationInterconnections,
NeatExpParams.PopulationSize)),
m_exp.PopulationEvaluator, m_exp.DefaultNeatParameters);
while (!m_shouldQuit)
{
Console.WriteLine("::::: Performing one generation");
Console.WriteLine();
m_evoAlg.PerformOneGeneration();
if (NeatExpParams.SaveFitnessGrowth)
{
m_eaLogger.WriteLine(String.Format("{0,-10} {1,-20} {2,-20} {3,-20}",
m_evoAlg.Generation,
m_evoAlg.BestGenome.Fitness,
m_evoAlg.Population.MeanFitness, m_evoAlg.Population.AvgComplexity));
}
m_curBestGenome = m_evoAlg.BestGenome as NeatGenome;
if (m_evoAlg.BestGenome.Fitness > m_overalBestFitness)
{
m_overalBestFitness = m_evoAlg.BestGenome.Fitness;
m_overalBestGenome = m_curBestGenome;
if (NeatExpParams.SaveEachGenerationChampionCPPN)
{
try
{
var doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)m_evoAlg.BestGenome);
var oFileInfo = new FileInfo(Path.Combine(
NeatExpParams.EALogDir, String.Format("BestIndividual-{0}-{1}.xml", MyUnum, m_evoAlg.Generation.ToString())));
doc.Save(oFileInfo.FullName);
}
catch
{
}
}
}
if (EAUpdate != null)
{
EAUpdate.Invoke(this, EventArgs.Empty);
}
}
}
public NoveltyThread(JSPopulationEvaluator jsPop, AssessGenotypeFunction assess, int popSize)
{
//save our objects for executing later!
popEval = jsPop;
populationSize = popSize;
autoEvent = new AutoResetEvent(false);
waitNextTime = true;
novelThread = new Thread(delegate()
{
autoEvent.WaitOne();
//we'll start by testing with 0 parents, and popsize of 15 yay!
noveltyRun = EvolutionManager.SharedEvolutionManager.initializeEvolutionAlgorithm(popEval, populationSize, assess);
//let our algoirhtm know we want to do novelty gosh darnit
if (noveltyRun.multiobjective != null)
{
noveltyRun.multiobjective.doNovelty = true;
}
//we make sure we don't wait in this loop, since we just got started!
waitNextTime = false;
while (true)
{
//this will cause us to pause!
if (waitNextTime)
{
waitNextTime = false;
autoEvent.WaitOne();
}
// Start the stopwatch we'll use to measure eval performance
Stopwatch sw = Stopwatch.StartNew();
//run the generation
noveltyRun.PerformOneGeneration();
// Stop the stopwatch
sw.Stop();
// Report the results
Console.WriteLine("Time used per gen (float): {0} ms", sw.Elapsed.TotalMilliseconds);
Console.WriteLine("Time used per gen (rounded): {0} ms", sw.ElapsedMilliseconds);
}
});
novelThread.Start();
}
public void oneGeneration(int currentGeneration)
{
DateTime dt = DateTime.Now;
ea.PerformOneGeneration();
if (ea.BestGenome.RealFitness > maxFitness)
{
simExperiment.bestGenomeSoFar = (NeatGenome)ea.BestGenome;
maxFitness = ea.BestGenome.RealFitness;
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome);
oFileInfo = new FileInfo(outputFolder + "bestGenome" + currentGeneration.ToString() + ".xml");
doc.Save(oFileInfo.FullName);
}
//Console.WriteLine(ea.Generation.ToString() + " " + ea.BestGenome.RealFitness + " " + ea.Population.GenomeList.Count + " " + (DateTime.Now.Subtract(dt)));
// Schrum: Changed this to include fitness values from each environment: Mainly for FourTasks
Console.WriteLine(ea.Generation.ToString() + " " + ea.BestGenome.RealFitness + " " + ea.Population.GenomeList.Count + " (" + string.Join(",", ea.BestGenome.Behavior.objectives) + ") " + (DateTime.Now.Subtract(dt)) + " " + ea.BestGenome.Behavior.modules + " " + ea.BestGenome.Behavior.cppnLinks + " " + ea.BestGenome.Behavior.substrateLinks);
int gen_mult = 200;
if (logging)
{
if (experiment.DefaultNeatParameters.noveltySearch && currentGeneration % gen_mult == 0)
{
XmlDocument archiveout = new XmlDocument();
XmlPopulationWriter.WriteGenomeList(archiveout, ea.noveltyFixed.archive);
oFileInfo = new FileInfo(outputFolder + "archive.xml");
archiveout.Save(oFileInfo.FullName);
}
if ((experiment.DefaultNeatParameters.noveltySearch || experiment.DefaultNeatParameters.multiobjective) && currentGeneration % gen_mult == 0)
{
XmlDocument popout = new XmlDocument();
if (!experiment.DefaultNeatParameters.multiobjective)
{
XmlPopulationWriter.Write(popout, ea.Population, ActivationFunctionFactory.GetActivationFunction("NullFn"));
}
else
{
XmlPopulationWriter.WriteGenomeList(popout, ea.multiobjective.population);
}
oFileInfo = new FileInfo(outputFolder + "population" + currentGeneration.ToString() + ".xml");
popout.Save(oFileInfo.FullName);
}
// Schrum: Added contents of objective array to log so individual environment scores can be seen in FourTasks domain
// Also always print modules, cppn links, and substrate links
logOutput.WriteLine(ea.Generation.ToString() + " " + (maxFitness).ToString() + " " + string.Join(" ", ea.BestGenome.Behavior.objectives) + " " + ea.BestGenome.Behavior.modules + " " + ea.BestGenome.Behavior.cppnLinks + " " + ea.BestGenome.Behavior.substrateLinks);
}
}
public void oneGeneration(int currentGeneration)
{
DateTime dt = DateTime.Now;
ea.PerformOneGeneration();
if (ea.BestGenome.RealFitness > maxFitness)
{
//simExperiment.bestGenomeSoFar = (NeatGenome)ea.BestGenome;
maxFitness = ea.BestGenome.RealFitness;
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome);
oFileInfo = new FileInfo(outputFolder + "bestGenome" + currentGeneration.ToString() + ".xml");
doc.Save(oFileInfo.FullName);
}
Console.WriteLine(ea.Generation.ToString() + " " + ea.BestGenome.RealFitness + " " + ea.Population.GenomeList.Count + " " + (DateTime.Now.Subtract(dt)));
int gen_mult = 200;
if (logging)
{
if (neatParams.noveltySearch && currentGeneration % gen_mult == 0)
{
XmlDocument archiveout = new XmlDocument();
XmlPopulationWriter.WriteGenomeList(archiveout, ea.noveltyFixed.archive);
oFileInfo = new FileInfo(outputFolder + "archive.xml");
archiveout.Save(oFileInfo.FullName);
}
if ((neatParams.noveltySearch || neatParams.multiobjective) && currentGeneration % gen_mult == 0)
{
XmlDocument popout = new XmlDocument();
if (!neatParams.multiobjective)
{
XmlPopulationWriter.Write(popout, ea.Population, ActivationFunctionFactory.GetActivationFunction("NullFn"));
}
else
{
XmlPopulationWriter.WriteGenomeList(popout, ea.multiobjective.population);
}
oFileInfo = new FileInfo(outputFolder + "population" + currentGeneration.ToString() + ".xml");
popout.Save(oFileInfo.FullName);
}
logOutput.WriteLine(ea.Generation.ToString() + " " + (maxFitness).ToString());
}
}
//private static Random random;
public static void Main(string[] args)
{
Util.Initialize(args[0]);
var idgen = new IdGenerator();
IExperiment experiment = new LimitExperiment();
XmlSerializer ser = new XmlSerializer(typeof(Settings));
//Settings settings = new Settings()
//{
// SmallBlind = 1,
// BigBlind = 2,
// GamesPerIndividual = 100,
// LogFile = "mutlithreaded_log.txt",
// MaxHandsPerTourney = 200,
// PlayersPerGame = 6,
// StackSize = 124,
// Threads = 4
//};
//ser.Serialize(new StreamWriter("settings.xml"), settings);
Settings settings = (Settings)ser.Deserialize(new StreamReader("settings.xml"));
var eval = new PokerPopulationEvaluator <SimpleLimitNeuralNetPlayer2, RingGamePlayerEvaluator>(settings);
var ea = new EvolutionAlgorithm(
new Population(idgen,
GenomeFactory.CreateGenomeList(experiment.DefaultNeatParameters,
idgen, experiment.InputNeuronCount,
experiment.OutputNeuronCount,
experiment.DefaultNeatParameters.pInitialPopulationInterconnections,
experiment.DefaultNeatParameters.populationSize)),
eval,
experiment.DefaultNeatParameters);
Console.WriteLine("Starting real evolution");
for (int i = 0; true; i++)
{
Console.WriteLine("Generation {0}", i + 1);
ea.PerformOneGeneration();
Console.WriteLine("Champion Fitness={0}", ea.BestGenome.Fitness);
var doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome);
FileInfo oFileInfo = new FileInfo("genomes_simple\\" + "bestGenome" + i.ToString() + ".xml");
doc.Save(oFileInfo.FullName);
}
}
/// <summary>
/// Runs one generation of the evolutionary algorithm.
/// </summary>
public void oneGeneration(int currentGeneration)
{
DateTime dt = DateTime.Now;
EA.PerformOneGeneration();
if (EA.BestGenome.RealFitness > MaxFitness)
{
SimExperiment.bestGenomeSoFar = (NeatGenome)EA.BestGenome;
MaxFitness = EA.BestGenome.RealFitness;
XmlDoc = new XmlDocument();
XmlGenomeWriterStatic.Write(XmlDoc, (NeatGenome)EA.BestGenome);
OutputFileInfo = new FileInfo(Path.Combine(OutputFolder, "bestGenome" + currentGeneration.ToString() + "_" + ((int)MaxFitness).ToString() + ".xml"));
XmlDoc.Save(OutputFileInfo.FullName);
}
if (experiment.DefaultNeatParameters.NS2 || experiment.DefaultNeatParameters.NSLC)
{
// The only reason NS2 has special output is so we can profile the novelty calc time versus the fitness calc time
Console.Write(EA.Generation.ToString() + " " + EA.BestGenome.RealFitness);
if (experiment.DefaultNeatParameters.NS1)
{
Console.Write(" nov: " + EA.ns1archiveAverageNovelty + " " + EA.Population.GenomeList.Count + "[" + EA.archiveSize + "]");
}
else
{
Console.Write(" nov: " + EA.averageNovelty + " " + EA.Population.GenomeList.Count + "[" + EA.archiveSize + "]");
}
if (experiment.DefaultNeatParameters.track_me_grid)
{
Console.Write("[" + EA.numFilledBins + "] " + EA.gridAverageFitness);
}
Console.WriteLine(" T-sim/nov/spe: " + EA.timeSpentInFitnessEval + " " + EA.timeSpentOutsideFitnessEval + " " + EA.timeSpentInSpeciation);
}
else if (experiment.DefaultNeatParameters.mapelites)
{
Console.WriteLine(EA.Generation.ToString() + " " + EA.BestGenome.RealFitness + " " + EA.Population.GenomeList.Count + "[" + EA.numFilledBins + "] " + EA.gridAverageFitness + " " + (DateTime.Now.Subtract(dt)));
}
else
{
Console.Write(EA.Generation.ToString() + " " + EA.BestGenome.RealFitness + " " + EA.Population.GenomeList.Count); // TODO: Add in average novelty
if (experiment.DefaultNeatParameters.track_me_grid)
{
Console.Write("[" + EA.numFilledBins + "] " + EA.gridAverageFitness);
}
Console.WriteLine(" " + (DateTime.Now.Subtract(dt)));
}
if (FinalPositionLogging)
{
foreach (IGenome g in EA.Population.GenomeList)
{
FinalPositionOutput.WriteLine(g.GenomeId + "," + g.Behavior.finalLocation[0] + "," + g.Behavior.finalLocation[1]);
}
}
if (TrajectoryLogging)
{
// If this is the first generation, create the new output folder
if (currentGeneration == 0)
{
TrajectoryFolderPath = Path.Combine(OutputFolder, "trajectories-" + FolderNum.ToString());
if (!Directory.Exists(TrajectoryFolderPath))
{
System.IO.Directory.CreateDirectory(TrajectoryFolderPath);
}
}
foreach (IGenome g in EA.Population.GenomeList)
{
// Check to see if we need to create a new folder
if (NumTrajectoriesRecorded == NumTrajectoriesPerFolder)
{
NumTrajectoriesRecorded = 0;
FolderNum++;
TrajectoryFolderPath = Path.Combine(OutputFolder, "trajectories-" + FolderNum.ToString());
if (!Directory.Exists(TrajectoryFolderPath))
{
System.IO.Directory.CreateDirectory(TrajectoryFolderPath);
}
}
// Print the Trajectory
using (System.IO.StreamWriter file = new System.IO.StreamWriter(Path.Combine(TrajectoryFolderPath, NumTrajectoriesRecorded.ToString() + ".txt"), true))
{
file.WriteLine("x,y");
foreach (double component in g.Behavior.trajectory)
{
file.Write(component.ToString() + ",");
}
}
// Increment the Trajectory counter
NumTrajectoriesRecorded++;
}
}
int gen_mult = 200;
if (Logging)
{
if (experiment.DefaultNeatParameters.noveltySearch && currentGeneration % gen_mult == 0)
{
XmlDocument archiveout = new XmlDocument();
XmlPopulationWriter.WriteGenomeList(archiveout, EA.noveltyFixed.archive);
OutputFileInfo = new FileInfo(Path.Combine(OutputFolder, "archive.xml"));
archiveout.Save(OutputFileInfo.FullName);
}
// If doing MapElites: print base500 generation instead of regular, and also print averageFitness and number of filled bins (instead of not)
// regular: generation bestfitness
// mapelites: generation500 bestfitness [numfilledgrids] averagefitness
if (experiment.DefaultNeatParameters.mapelites)
{
LogOutput.WriteLine(EA.generationBase500.ToString() + " " + (MaxFitness).ToString() + " " + EA.numFilledBins.ToString() + " " + EA.averageFitness.ToString());
LogOutput.Flush();
if (FinalPositionLogging)
{
foreach (IGenome g in EA.addedToArchive)
{
ArchiveModificationOutput.WriteLine(g.GenomeId + ",+," + EA.numEvaluations + "," + g.Behavior.finalLocation[0] + "," + g.Behavior.finalLocation[1]);
}
EA.addedToArchive.Clear();
foreach (IGenome g in EA.removedFromArchive)
{
ArchiveModificationOutput.WriteLine(g.GenomeId + ",-," + EA.numEvaluations + "," + g.Behavior.finalLocation[0] + "," + g.Behavior.finalLocation[1]);
}
EA.removedFromArchive.Clear();
}
}
else if (experiment.DefaultNeatParameters.NS2 || experiment.DefaultNeatParameters.NSLC)
{
LogOutput.Write(EA.generationBase500.ToString() + " " + (MaxFitness).ToString() + " " + EA.archiveSize + " " + EA.averageFitness.ToString() + " " + (EA.ns1 ? EA.ns1archiveAverageNovelty.ToString() : EA.averageNovelty.ToString()));
if (experiment.DefaultNeatParameters.track_me_grid)
{
LogOutput.Write(" " + EA.numFilledBins.ToString() + " " + EA.gridAverageFitness.ToString());
}
LogOutput.WriteLine();
LogOutput.Flush();
if (FinalPositionLogging)
{
foreach (IGenome g in EA.addedToArchive)
{
ArchiveModificationOutput.WriteLine(g.GenomeId + ",+," + EA.numEvaluations + "," + g.Behavior.finalLocation[0] + "," + g.Behavior.finalLocation[1]);
}
EA.addedToArchive.Clear();
foreach (IGenome g in EA.removedFromArchive)
{
ArchiveModificationOutput.WriteLine(g.GenomeId + ",-," + EA.numEvaluations + "," + g.Behavior.finalLocation[0] + "," + g.Behavior.finalLocation[1]);
}
EA.removedFromArchive.Clear();
}
}
else
{
LogOutput.Write(EA.generationBase500.ToString() + " " + (MaxFitness).ToString());
if (experiment.DefaultNeatParameters.track_me_grid)
{
LogOutput.Write(" " + EA.numFilledBins.ToString() + " " + EA.gridAverageFitness.ToString());
}
LogOutput.WriteLine();
LogOutput.Flush();
}
// Output complexity statistics
ComplexityOutput.WriteLine(EA.avgComplexity + ", " + EA.stdevComplexity + ", " + EA.minComplexity + ", " + EA.maxComplexity);
ComplexityOutput.Flush();
}
}
/// <summary>
/// Performs one tick of the simulation. This function is called automatically on loop by the game engine.
/// </summary>
/// <param name="gameTime"></param>
protected override void Update(GameTime gameTime)
{
// base.Update() will call the Creature.Update() function
base.Update(gameTime);
if (!paused)
{
bool stop = false;
// Stopping conditions:
// 1) we are at the beginning of a first trial and there are already entries in the behavior characterization vector
if (numUpdates == 0 && ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Behavior.behaviorList.Count != 0)
{
stop = true;
}
// 2) we have exceeded the number of max time steps
else if ((!bidirectionalTrials && (numUpdates > maxTimeSteps)) || (bidirectionalTrials && (numUpdates > (2 * maxTimeSteps))))
{
stop = true;
}
// 3) we are freezing after planted and someone has planted
else if (freezeAfterPlanting && ((firstTrial && (plantedInColoredSpace1 || plantedInWhiteSpace1)) || (!firstTrial && (plantedInColoredSpace2 || plantedInWhiteSpace2))))
{
stop = true;
}
if (stop)
{
// If we're stopping, first check to see if we need to perform another generation of the EA
if (GenomeIndexOfCurrentCreature == ea.Population.GenomeList.Count - 1)
{
// Write the just-completed generation's data to XML before it is lost
using (System.IO.StreamWriter file = new System.IO.StreamWriter("RunInfo.txt", true))
file.WriteLine("Generation " + generation + " completed at " + DateTime.Now.ToString("HH:mm:ss tt"));
// Perform one run of the EA now that we have behavioral data
ea.noveltyFixed.measure_against = ea.Population.GenomeList;
ea.PerformOneGeneration();
// Increase the generation counter, now that we've written the last one
generation++;
if (fixedIndividuals && generation * populationSize >= numIndividuals)
{
Exit();
}
else
{
// Reset the index counter and start the next generation from the beginning
GenomeIndexOfCurrentCreature = -1;
ResetToFirstTrial();
return;
}
}
// Otherwise we can just rest to the first trial
else
{
ResetToFirstTrial();
return;
}
}
// If we're in a bidirectional search, we may need to being the second trial
if (bidirectionalTrials && (numUpdates == (maxTimeSteps - 1)))
{
beginSecondTrial();
return;
}
// Update the behavior vector for the creature that is currently be being evaluated
numUpdates++;
ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Behavior.behaviorList.Add(currentCreature.Position.X);
ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Behavior.behaviorList.Add(currentCreature.Position.Y);
ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Behavior.behaviorList.Add(currentCreature.Heading);
// If the creature has planted itself, decide whether or not it planted itself in a valid position
// and tidy up some other business.
if (currentCreature.currentState.Equals(State.Planting))
{
// Append 1 to the behavior vector if the creature is planting
ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Behavior.behaviorList.Add(1.0);
if (currentCreature.isAtValidPlantingLocation())
{
if (firstTrial)
{
plantedInColoredSpace1 = true;
}
else
{
plantedInColoredSpace2 = true;
}
}
else
{
if (firstTrial)
{
plantedInWhiteSpace1 = true;
}
else
{
plantedInWhiteSpace2 = true;
}
}
}
else
{
// Otherwise append 0 to the behavior vector if the creature is not moving
ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Behavior.behaviorList.Add(0.0);
}
}
}
public static void ThreeDHyperNEAT()
{
if (!cmdParser.IsArgumentProvided("-shape", out Program.Shape))
{
Program.Shape = "triangle";
}
if (!cmdParser.IsArgumentProvided("-folder", out Program.LogFolder))
{
Program.LogFolder = "TestLogs";
}
if (!Directory.Exists(Program.LogFolder))
{
Directory.CreateDirectory(Program.LogFolder);
}
if (!cmdParser.IsIntArgumentProvided("-gens", out Program.MaxGenerations))
{
Program.MaxGenerations = 1000;
}
double maxFitness = 0;
int maxGenerations = Program.MaxGenerations;
int populationSize = Program.PopulationSize;
IExperiment exp = new Skirmish3DExperiment(5, Program.Shape, Program.PopulationSize);
StreamWriter sw = File.CreateText(Path.Combine(Program.LogFolder,
String.Format("{0}-logfile.log", Program.LogFolder)));
sw.AutoFlush = true;
XmlDocument doc;
FileInfo oFileInfo;
IdGenerator idgen;
EvolutionAlgorithm ea;
idgen = new IdGenerator();
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(exp.DefaultNeatParameters, idgen, exp.InputNeuronCount, exp.OutputNeuronCount, exp.DefaultNeatParameters.pInitialPopulationInterconnections, populationSize)), exp.PopulationEvaluator, exp.DefaultNeatParameters);
for (int j = 0; j < maxGenerations; j++)
{
DateTime dt = DateTime.Now;
ea.PerformOneGeneration();
if (ea.BestGenome.Fitness > maxFitness)
{
maxFitness = ea.BestGenome.Fitness;
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome);
oFileInfo = new FileInfo(Path.Combine(Program.LogFolder, "BestGenome-" + j.ToString() + ".xml"));
doc.Save(oFileInfo.FullName);
// This will output the substrate, uncomment if you want that
/* doc = new XmlDocument();
* XmlGenomeWriterStatic.Write(doc, (NeatGenome) SkirmishNetworkEvaluator.substrate.generateMultiGenomeModulus(ea.BestGenome.Decode(null),5));
* oFileInfo = new FileInfo(folder + "bestNetwork" + j.ToString() + ".xml");
* doc.Save(oFileInfo.FullName);
*/
}
Console.WriteLine(ea.Generation.ToString() + " " + ea.BestGenome.Fitness + " " + (DateTime.Now.Subtract(dt)));
//Do any post-hoc stuff here
sw.WriteLine("{0} {1} {2}", ea.Generation, maxFitness, ea.Population.MeanFitness);
sw.Flush();
}
sw.Close();
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome, ActivationFunctionFactory.GetActivationFunction("NullFn"));
oFileInfo = new FileInfo(Path.Combine(Program.LogFolder, "BestGenome.xml"));
doc.Save(oFileInfo.FullName);
}
private static void PredatorCCEAHNMainThread(object oAgentId)
{
int agentId = (int)oAgentId;
double maxFitness = 0;
int maxGenerations = Program.MaxGenerations;
int populationSize = Program.PopulationSize;
IExperiment exp = new SkirmishCCEAExperiment(agentId, Program.PopulationSize);
StreamWriter sw = File.CreateText(Path.Combine(Program.LogFolder,
String.Format("{0}-logfile-{1}.log", Program.LogFolder, agentId)));
sw.AutoFlush = true;
XmlDocument doc;
FileInfo oFileInfo;
IdGenerator idgen = new IdGenerator();
EvolutionAlgorithm ea = new EvolutionAlgorithm(
new Population(idgen,
GenomeFactory.CreateGenomeList(exp.DefaultNeatParameters,
idgen, exp.InputNeuronCount, exp.OutputNeuronCount,
exp.DefaultNeatParameters.pInitialPopulationInterconnections,
populationSize)),
exp.PopulationEvaluator, exp.DefaultNeatParameters);
for (int j = 0; j < maxGenerations; j++)
{
DateTime dt = DateTime.Now;
ea.PerformOneGeneration();
if (ea.BestGenome.Fitness > maxFitness)
{
maxFitness = ea.BestGenome.Fitness;
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome);
oFileInfo = new FileInfo(Path.Combine(LogFolder,
String.Format("BestGenome-{0}-{1}.xml", agentId, j)));
doc.Save(oFileInfo.FullName);
// This will output the substrate, uncomment if you want that
/* doc = new XmlDocument();
* XmlGenomeWriterStatic.Write(doc, (NeatGenome) SkirmishNetworkEvaluator.substrate.generateMultiGenomeModulus(ea.BestGenome.Decode(null),5));
* oFileInfo = new FileInfo(folder + "bestNetwork" + j.ToString() + ".xml");
* doc.Save(oFileInfo.FullName);
*/
}
Console.WriteLine("[{0}] {1} {2} {3}", agentId, ea.Generation, ea.BestGenome.Fitness, (DateTime.Now.Subtract(dt)));
//Do any post-hoc stuff here
sw.WriteLine("{0} {1} {2}", ea.Generation, maxFitness, ea.Population.MeanFitness);
sw.Flush();
}
sw.Close();
//doc = new XmlDocument();
//XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome, ActivationFunctionFactory.GetActivationFunction("NullFn"));
//oFileInfo = new FileInfo(Path.Combine(LogFolder, "BestGenome.xml"));
//doc.Save(oFileInfo.FullName);
Environment.Exit(0);
}
