/// <summary>
/// Performs multiple generations of the evolutionary algorithm.
/// </summary>
public void evolve(int generations)
{
for (int j = 0; j < generations; j++)
{
oneGeneration(j);
}
LogOutput.Close();
XmlDoc = new XmlDocument();
XmlGenomeWriterStatic.Write(XmlDoc, (NeatGenome)EA.BestGenome, ActivationFunctionFactory.GetActivationFunction("NullFn"));
OutputFileInfo = new FileInfo(Path.Combine(OutputFolder, "bestGenome.xml"));
XmlDoc.Save(OutputFileInfo.FullName);
//if doing novelty search, write out archive
if (experiment.DefaultNeatParameters.noveltySearch)
{
XmlDocument archiveout = new XmlDocument();
XmlPopulationWriter.WriteGenomeList(archiveout, EA.noveltyFixed.archive);
OutputFileInfo = new FileInfo(Path.Combine(OutputFolder, "archive.xml"));
archiveout.Save(OutputFileInfo.FullName);
}
// dump the MapElites grid contents (empty if we aren'type doing ME and we aren'type tracking ME-style-grid for some other algorithm)
XmlDocument gridout = new XmlDocument();
XmlPopulationWriter.WriteGenomeList(gridout, EA.meDumpGrid());
OutputFileInfo = new FileInfo(Path.Combine(OutputFolder, "finalgrid.xml"));
gridout.Save(OutputFileInfo.FullName);
}
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());
}
}
public void printDistribution()
{
String filename = "dist" + generation + ".txt";
string content = "";
XmlDocument archiveout = new XmlDocument();
XmlPopulationWriter.WriteGenomeList(archiveout, population);
archiveout.Save(filename);
/*
* for(int i=0;i<population.Count;i++) {
* for(int j=0;j<population[i].objectives.Length;j++)
* content+=population[i].objectives[j]+" ";
* content+=population[i].Fitness+"\n";
* }
* System.IO.File.WriteAllText(filename,content);
*/
}
public void evolve(int generations)
{
for (int j = 0; j < generations; j++)
{
oneGeneration(j);
}
logOutput.Close();
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome, ActivationFunctionFactory.GetActivationFunction("NullFn"));
oFileInfo = new FileInfo(outputFolder + "bestGenome.xml");
doc.Save(oFileInfo.FullName);
//if doing novelty search, write out archive
if (neatParams.noveltySearch)
{
XmlDocument archiveout = new XmlDocument();
XmlPopulationWriter.WriteGenomeList(archiveout, ea.noveltyFixed.archive);
oFileInfo = new FileInfo(outputFolder + "archive.xml");
archiveout.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();
}
}
