/// <summary>
/// Creates a genomeList from a random start, or using a list of seed genome IDs
/// </summary>
/// <param name="popSize"></param>
/// <param name="parentGenomeIDs"></param>
/// <returns></returns>
public GenomeList createGenomeList(int popSize, AssessGenotypeFunction genoAssess, List <long> parentGenomeIDs = null)
{
//must return a genome list!
GenomeList gl;
//if we have parents, add their genomes to our starting genomelist
if (parentGenomeIDs != null)
{
GenomeList seeds = new GenomeList(); seeds.AddRange(getGenomesFromIDs(parentGenomeIDs).Values);
gl = new GenomeList();
gl.AddRange(GenomeFactory.CreateGenomeListPreserveIDs(seeds, popSize, neatParams, idgen, genoAssess));
}
else
{
//we don't have any seeds, we need to form our own initial population
gl = GenomeFactory.CreateGenomeListPreserveIDs(neatParams, idgen,
cppnInputs, cppnOutputs, neatParams.pInitialPopulationInterconnections, popSize,
genoAssess);
}
//add each genome to our list of generated genomes, yo.
//gl.ForEach(genome => allGeneratedGenomes.Add(genome));
//now we are free to return the genomes
return(gl);
}
public void initializeEvolution(int populationSize)
{
logOutput = new StreamWriter(outputFolder + "logfile.txt");
IdGenerator idgen = new IdGenerator();
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(experiment.DefaultNeatParameters, idgen, experiment.InputNeuronCount, experiment.OutputNeuronCount, experiment.OutputsPerPolicy, experiment.DefaultNeatParameters.pInitialPopulationInterconnections, populationSize)), experiment.PopulationEvaluator, experiment.DefaultNeatParameters);
}
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 void initializeEvolution(int populationSize, NeatGenome seedGenome)
{
if (seedGenome == null)
{
initializeEvolution(populationSize);
return;
}
logOutput = new StreamWriter(outputFolder + "logfile.txt");
IdGenerator idgen = new IdGeneratorFactory().CreateIdGenerator(seedGenome);
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(seedGenome, populationSize, experiment.DefaultNeatParameters, idgen)), experiment.PopulationEvaluator, experiment.DefaultNeatParameters);
}
public ElitistEvolution(int populationSize, int archiveSize, GenomeFactory factory, Environment environment, int numAttributes, TextWriter writer)
{
var random = new SystemRandom(42);
var selection = new BinaryTournamentSelection(random);
this.numAttributes = numAttributes;
this.archiveSize = archiveSize;
this.evolution = new Evolution(populationSize, 0.5, 0.4, factory, selection);
this.environment = environment;
this.ranking = new CrowdingDistanceRanking(numAttributes);
this.writer = writer;
}
public void initializeEvolution(int populationSize)
{
if (logOutput != null)
{
logOutput.Close();
}
logOutput = new StreamWriter(outputFolder + "logfile.txt");
IdGenerator idgen = new IdGenerator();
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(neatParams, idgen, cppnInputs, cppnOutputs, neatParams.pInitialPopulationInterconnections, populationSize)), populationEval, neatParams);
}
void Awake()
{
if (instance == null)
{
instance = this;
}
else
{
Destroy(gameObject);
}
random = new System.Random();
}
//factory method
public ICarBrainController CreateCarBrainController(List <decimal> genomeWeights)
{
Genome genome = null;
if (genomeWeights == null)
{
genome = GenomeFactory.CreateRandom();
}
genome = GenomeFactory.Create(genomeWeights);
var neuralNetwork = new NeuralNetwork();
neuralNetwork.LoadGenome(genome);
return(new CarBrainController(neuralNetwork));
}
public void CreateGenomes(int populationSize)
{
if (GenomeFactory == null)
{
throw new ApplicationException("Impossível criar genomas no algoritimo genético com o GenomeFactory nulo!");
}
this.populationSize = populationSize;
Genome[] genomes = new Genome[populationSize];
for (int i = 0; i < populationSize; i++)
{
gnomes.Add(GenomeFactory.CreateGenome(this));
}
}
public void initializeEvolution(int populationSize, NeatGenome seedGenome)
{
if (seedGenome == null)
{
initializeEvolution(populationSize);
return;
}
if (logOutput != null)
{
logOutput.Close();
}
logOutput = new StreamWriter(outputFolder + "logfile.txt");
IdGenerator idgen = new IdGeneratorFactory().CreateIdGenerator(seedGenome);
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(seedGenome, populationSize, neatParams, idgen)), populationEval, neatParams);
}
public IGenome fetchRandomNetwork()
{
//we create our objects, making sure any unique node or connection is saved in WIN
//(in reality, after the first few individuals, there shouldn't be any new nodes or connections created from random networks)
//if we have a seed, EVERYTHIGN MUST COME FROM TEH SEED
if (officialSeedGenome != null)
{
return(GenomeFactory.CreateGenomePreserveID(officialSeedGenome, idgen));
}
//we need to verify that we aren't duplicating nodes or connections while generating this object, and that at the end of this call
//the idgenerator hasn't been destroyed! In the future, we'll phase out use of the idgenerator, in favor of WIN, but not yet.
return(GenomeFactory.CreateGenomePreserveID(neatParams, idgen, cppnInputs, cppnOutputs, neatParams.pInitialPopulationInterconnections));
//big bada boom
}
//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>
/// Initializes the EA with a random intial population.
/// </summary>
public void initializeEvolution(int populationSize)
{
LogOutput = Logging ? new StreamWriter(Path.Combine(OutputFolder, "log.txt")) : null;
FinalPositionOutput = FinalPositionLogging ? new StreamWriter(Path.Combine(OutputFolder, "final-position.txt")) : null;
ArchiveModificationOutput = FinalPositionLogging ? new StreamWriter(Path.Combine(OutputFolder, "archive-mods.txt")) : null;
ComplexityOutput = new StreamWriter(Path.Combine(OutputFolder, "complexity.txt"));
ComplexityOutput.WriteLine("avg,stdev,min,max");
if (FinalPositionLogging)
{
FinalPositionOutput.WriteLine("ID,x,y");
ArchiveModificationOutput.WriteLine("ID,action,time,x,y");
}
IdGenerator idgen = new IdGenerator();
EA = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(experiment.DefaultNeatParameters, idgen, experiment.InputNeuronCount, experiment.OutputNeuronCount, experiment.DefaultNeatParameters.pInitialPopulationInterconnections, populationSize, SimExperiment.neatBrain)), experiment.PopulationEvaluator, experiment.DefaultNeatParameters);
EA.outputFolder = OutputFolder;
EA.neatBrain = NEATBrain;
}
/// <summary>
/// Create an initial random population.
/// </summary>
public void Reset()
{
// create the genome factory
if (IsHyperNEAT)
{
CODEC = new HyperNEATCODEC();
GenomeFactory = new FactorHyperNEATGenome();
}
else
{
CODEC = new NEATCODEC();
GenomeFactory = new FactorNEATGenome();
}
// create the new genomes
Species.Clear();
// reset counters
GeneIdGenerate.CurrentID = 1;
InnovationIDGenerate.CurrentID = 1;
EncogRandom rnd = RandomNumberFactory.Factor();
// create one default species
BasicSpecies defaultSpecies = new BasicSpecies();
defaultSpecies.Population = this;
// create the initial population
for (int i = 0; i < PopulationSize; i++)
{
NEATGenome genome = GenomeFactory.Factor(rnd, this,
InputCount, OutputCount,
InitialConnectionDensity);
defaultSpecies.Add(genome);
}
defaultSpecies.Leader = defaultSpecies.Members[0];
Species.Add(defaultSpecies);
// create initial innovations
Innovations = new NEATInnovationList(this);
}
// Adaptive GAs
// In CAGA (clustering-based adaptive genetic algorithm)
void Start()
{
settings = GetComponent <GASettings>();
populationSize = settings.PopulationSize;
nrGenerations = settings.NumberOfGenerations;
blocking = settings.RunAsFastAsPossible;
environment.Settings = settings;
selectionStrategy.Settings = settings;
matingStrategy.Settings = settings;
mutationStrategy.Settings = settings;
population = new Population(populationSize);
selectionBuffer = new SelectionBuffer(populationSize);
statistics = GetComponent <Statistics>();
if (statistics == null)
{
statistics = gameObject.AddComponent <Statistics>();
}
statistics.Population = population;
BaseGenome genomeTemplate = GenomeFactory.CreateGenome(genomeType, genomeSize);
for (int i = 0; i < populationSize; i++)
{
BaseGenome genome = genomeTemplate.CreateRandom(); // Should be possible to generate these by hand (seeded)
population[i] = new PhenomeDescription(genome);
}
for (int i = 0; i < populationSize; i++)
{
BaseGenome genome = genomeTemplate.CreateRandom(); // Should be possible to generate these by hand (seeded)
selectionBuffer[i] = genome;
}
nextStepDelegate = NextStep;
currentState = State.FitnessTest;
environment.Inititalize(population);
}
/// <summary>
/// Initializes the EA with an initial population generated from a single seed genome.
/// </summary>
public void initializeEvolution(int populationSize, NeatGenome seedGenome)
{
if (seedGenome == null)
{
initializeEvolution(populationSize);
return;
}
LogOutput = Logging ? new StreamWriter(Path.Combine(OutputFolder, "log.txt")) : null;
FinalPositionOutput = FinalPositionLogging ? new StreamWriter(Path.Combine(OutputFolder, "final-position.txt")) : null;
ArchiveModificationOutput = FinalPositionLogging ? new StreamWriter(Path.Combine(OutputFolder, "archive-mods.txt")) : null;
ComplexityOutput = new StreamWriter(Path.Combine(OutputFolder, "complexity.txt"));
ComplexityOutput.WriteLine("avg,stdev,min,max");
if (FinalPositionLogging)
{
FinalPositionOutput.WriteLine("x,y");
ArchiveModificationOutput.WriteLine("ID,action,time,x,y");
}
IdGenerator idgen = new IdGeneratorFactory().CreateIdGenerator(seedGenome);
EA = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(seedGenome, populationSize, experiment.DefaultNeatParameters, idgen)), experiment.PopulationEvaluator, experiment.DefaultNeatParameters);
EA.outputFolder = OutputFolder;
EA.neatBrain = NEATBrain;
}
public AllOnesPhenome()
{
Genome = GenomeFactory.CreateGenome(GenomeType.RawBinaryString64, 64);
}
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);
}
void buildBodyExamples()
{
//we need to create random genomes, then save their generated bodies!
NeatParameters np = new NeatParameters();
IdGenerator idGen = new IdGenerator();
idGen.ResetNextInnovationNumber();
Random r = new Random();
JObject root = new JObject();
JObject meta = new JObject();
JArray genomeArray = new JArray();
//how many random input tests?
int genomeCount = 20;
meta.Add("genomeCount", genomeCount);
meta.Add("weightRange", HyperNEATParameters.weightRange);
NeatGenome seed = EvolutionManager.SharedEvolutionManager.getSeed();
int tEmpty = 0;
int emptyCount = genomeCount / 4;
for (int n = genomeArray.Count; n < genomeCount; n = genomeArray.Count)
{
//create our genome
var inputCount = r.Next(4) + 3;
var outputCount = r.Next(3) + 1;
//radnom inputs 3-6, random outputs 1-3
var genome = GenomeFactory.CreateGenomePreserveID(seed, idGen);//np, idGen, inputCount, outputCount, 1);
Hashtable nodeHT = new Hashtable();
Hashtable connHT = new Hashtable();
//mutate our genome
for (int m = 0; m < 20; m++)
{
((NeatGenome)genome).Mutate(np, idGen, nodeHT, connHT);
}
//now turn genome into a network
var network = genome.Decode(null);
//turn network into JSON, and save as the network object
//genomeJSON.Add("network", JObject.FromObject(network));
//now we need a body
bool isEmptyBody;
//convert to body object
var bodyObject = simpleCom.simpleExperiment.genomeIntoBodyObject(genome, out isEmptyBody);
if ((isEmptyBody && tEmpty++ < emptyCount) || (!isEmptyBody))
{
//create object and add body info to it, then save it in our array
JObject genomeJSON = new JObject();
genomeJSON.Add("genome", JObject.FromObject(genome, new JsonSerializer()
{
ReferenceLoopHandling = ReferenceLoopHandling.Ignore
}));
genomeJSON.Add("network", JObject.FromObject(network, new JsonSerializer()
{
ReferenceLoopHandling = ReferenceLoopHandling.Ignore
}));
//save our body object from test
genomeJSON.Add("body", JObject.FromObject(bodyObject));
genomeJSON.Add("isEmpty", isEmptyBody.ToString());
//finally, we add our network json to the body array
genomeArray.Add(genomeJSON);
}
}
//add our networks, and add our meta information
root.Add("genomeCount", genomeArray.Count);
root.Add("genomes", genomeArray);
root.Add("meta", meta);
//and away we go! Let's save to file!
using (System.IO.StreamWriter file = new System.IO.StreamWriter("testgenomebodies.json"))
{
file.WriteLine(root.ToString());
}
}
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);
}
void generateSampleCPPNs()
{
NeatParameters np = new NeatParameters();
IdGenerator idGen = new IdGenerator();
idGen.ResetNextInnovationNumber();
Random r = new Random();
JObject root = new JObject();
JObject meta = new JObject();
JArray networkArray = new JArray();
//how many random input tests?
int testCount = 100;
int networkCount = 20;
meta.Add("networkCount", networkCount);
meta.Add("sampleCount", testCount);
Console.WriteLine("All Networks start will run:" + networkCount * testCount);
for (int n = 0; n < networkCount; n++)
{
Console.WriteLine("Network start:" + n);
JObject networkJSON = new JObject();
//create our genome
var inputCount = r.Next(4) + 3;
var outputCount = r.Next(3) + 1;
//radnom inputs 3-6, random outputs 1-3
var genome = GenomeFactory.CreateGenome(np, idGen, inputCount, outputCount, 1);
Console.WriteLine("Genoem created:" + n);
Hashtable nodeHT = new Hashtable();
Hashtable connHT = new Hashtable();
//mutate our genome
for (int m = 0; m < 20; m++)
{
((NeatGenome)genome).Mutate(np, idGen, nodeHT, connHT);
Console.WriteLine("Mutation done: " + m);
}
Console.WriteLine("Mutations done:" + n);
//now turn genome into a network
var network = genome.Decode(null);
Console.WriteLine("genome decoded:" + n);
//turn network into JSON, and save as the network object
networkJSON.Add("network", JObject.FromObject(network));
JArray inputsAndOutputs = new JArray();
Console.WriteLine("starting tests:" + n);
for (var t = 0; t < testCount; t++)
{
Console.WriteLine("Test " + t + " :" + "for" + n);
JArray inputSamples = new JArray();
JArray outputSamples = new JArray();
network.ClearSignals();
Console.WriteLine("Testclear " + t + " :" + "for" + n);
//var saveInputs = new float[inputCount];
for (int ins = 0; ins < inputCount; ins++)
{
//inputs from -1,1
var inF = (float)(2 * r.NextDouble() - 1);
//saveInputs[ins] = inF;
network.SetInputSignal(ins, inF);
//add our random input
inputSamples.Add(JToken.FromObject(inF));
}
Console.WriteLine("Testrecursive next" + t + " :" + "for" + n);
//run our network, and save the response
((ModularNetwork)network).RecursiveActivation();
//network.MultipleSteps(30);
Console.WriteLine("recursive done " + t + " :" + "for" + n);
//var saveOuts = new float[outputCount];
for (var outs = 0; outs < outputCount; outs++)
{
//saveOuts[outs] = network.GetOutputSignal(outs);
//keep our outputs in an output array
outputSamples.Add(JToken.FromObject(network.GetOutputSignal(outs)));
}
//network.ClearSignals();
//network.SetInputSignals(saveInputs);
//network.MultipleSteps(30);
////((ModularNetwork)network).RecursiveActivation();
//for (var outs = 0; outs < outputCount; outs++)
//{
// Console.WriteLine("Difference in activation: " + Math.Abs(network.GetOutputSignal(outs) - saveOuts[outs]));
//}
Console.WriteLine("test reached past outputs " + t + " :" + "for" + n);
JObject test = new JObject();
test.Add("inputs", inputSamples);
test.Add("outputs", outputSamples);
inputsAndOutputs.Add(test);
Console.WriteLine("Ins/outs done " + t + " :" + "for" + n);
}
Console.WriteLine("tests ended:" + n);
//we add our inputs/outs for this json network
networkJSON.Add("tests", inputsAndOutputs);
//finally, we add our network json to the network array
networkArray.Add(networkJSON);
Console.WriteLine("Network finished:" + n);
}
Console.WriteLine("All newtorks finished, cleaning up");
//add our networks, and add our meta information
root.Add("networks", networkArray);
root.Add("meta", meta);
//and away we go! Let's save to file!
using (System.IO.StreamWriter file = new System.IO.StreamWriter("testgenomes.json"))
{
file.WriteLine(root.ToString());
}
}
static void Main(string[] args)
{
NeatGenome seedGenome = null;
string filename = @"seedGenome.xml";
try
{
XmlDocument document = new XmlDocument();
document.Load(filename);
seedGenome = XmlNeatGenomeReaderStatic.Read(document);
}
catch (Exception e)
{
System.Console.WriteLine("Problem loading genome. \n" + e.Message);
}
string experimentName = "_2Pref";
for (int k = 3; k < 10; k++)
{
double maxFitness = 0;
int maxGenerations = 400; //150;
int populationSize = 300; //100;//150;
Thread extraWindowThread;
extraWindowThread = new System.Threading.Thread(delegate()
{
var myForm = new SharpNeatExperiments.Pacman.MyForm1();
System.Windows.Forms.Application.Run(myForm);
});
extraWindowThread.Start();
IExperiment exp = null;
//exp = new SUPGONLYExperiment(4,12,12,5,2);
//exp = new PacmanExperimentSUPG(4, 12, 12, 5, 2);
//exp = new SPMMExperiment(4, 12, 12, 5, 2);
//exp = new SPCExperiment(4, 12, 12, 5, 2);
exp = new SPSUPGExperiment(4, 12, 12, 5, 6);
StreamWriter SW;
SW = File.CreateText("logfile.txt");
//Change this line for different experiments
XmlDocument doc;
FileInfo oFileInfo;
IdGenerator idgen;
EvolutionAlgorithm ea;
if (seedGenome == null)
{
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);
}
else
{
idgen = new IdGeneratorFactory().CreateIdGenerator(seedGenome);
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(seedGenome, exp.DefaultNeatParameters.populationSize, exp.DefaultNeatParameters, idgen)), exp.PopulationEvaluator, exp.DefaultNeatParameters);
}
bool isNSGAiiEnabled = false;
for (int j = 0; j < maxGenerations; j++)
{
DateTime dt = DateTime.Now;
if (isNSGAiiEnabled)
{
ea.PerformOneGenerationNSGAii();
}
else
{
ea.PerformOneGeneration();
}
if (ea.BestGenome.Fitness > maxFitness)
{
maxFitness = ea.BestGenome.Fitness;
Console.WriteLine(maxFitness + "maxFitness");
Console.WriteLine("objectiveFitness" + ea.BestGenome.MultiObjectiveFitness[0] + " " + ea.BestGenome.MultiObjectiveFitness[1]);
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome);
oFileInfo = new FileInfo("bestGenome" + j.ToString() + ".xml");
doc.Save(oFileInfo.FullName);
//MadsXMLWriter(j, maxFitness);
}
MadsXMLWriter(j, maxFitness, k, experimentName);
Console.WriteLine(ea.Generation.ToString() + " " + (maxFitness).ToString() + " " + (DateTime.Now.Subtract(dt)));
//Do any post-hoc stuff here
SW.WriteLine(ea.Generation.ToString() + " " + (maxFitness).ToString());
}
SW.Close();
//----- Write the genome to an XmlDocument.
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome, ActivationFunctionFactory.GetActivationFunction("NullFn"));
oFileInfo = new FileInfo("bestGenome" + experimentName + "_" + k + ".xml");
doc.Save(oFileInfo.FullName);
}
}
public Evolution(int populationSize, double crossoverRate, double mutationRate, GenomeFactory factory, Selection selection)
{
this.populationSize = populationSize;
this.crossoverQuota = (int)(populationSize * crossoverRate);
this.mutationQuota = (int)(populationSize * mutationRate);
this.factory = factory;
this.selection = selection;
}
public CarPhenome()
{
Genome = GenomeFactory.CreateGenome(GenomeType.FloatString, (5 * 8 + 8) + (8 * 3 + 3));
}
public CardPhenome()
{
Genome = GenomeFactory.CreateGenome(GenomeType.RawBinaryString32, 10);
}
public GeneticAlgorithmSettings(GenomeFactory genomeFactory)
{
mGenomeFactory = genomeFactory;
}
public void run(int type, int evaluationMethod, System.Threading.CancellationToken token)
{
double maxFitness = 0;
int maxGenerations = 800;
int populationSize = 300;
IExperiment exp;
if (evaluationMethod == 1)
{
novelty = true;
}
if (type == 0)
{
exp = new CTRNNExperiment(4, 12, 8, 8, 4);
}
else if (type == 1)
{
exp = new SUPGExperiment(4, 12, 12, 3, 2);
}
else
{
doClune = true;
exp = new CluneExperiment(20, 20, 20, 6, 1);
}
XmlDocument doc;
FileInfo oFileInfo;
IdGenerator idgen;
EvolutionAlgorithm ea;
NeatGenome seedGenome = null;
if (seedGenome == null)
{
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);
}
else
{
idgen = new IdGeneratorFactory().CreateIdGenerator(seedGenome);
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(seedGenome, populationSize, exp.DefaultNeatParameters, idgen)), exp.PopulationEvaluator, exp.DefaultNeatParameters);
}
Directory.CreateDirectory(folder);
using (var logWriter = File.CreateText(folder + "Log " + DateTime.Now.ToString("u").Replace(':', '.') + ".txt"))
for (int j = 0; j < maxGenerations; j++)
{
if (token.IsCancellationRequested)
{
logWriter.WriteLine("Cancelled");
break;
}
DateTime dt = DateTime.Now;
ea.PerformOneGeneration();
if (ea.BestGenome.ObjectiveFitness > maxFitness)
{
maxFitness = ea.BestGenome.ObjectiveFitness;
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome);
oFileInfo = new FileInfo(folder + "bestGenome" + j + ".xml");
doc.Save(oFileInfo.FullName);
/*/ This will output the substrate
* doc = new XmlDocument();
* XmlGenomeWriterStatic.Write(doc, SUPGNetworkEvaluator.substrate.generateGenome(ea.BestGenome.Decode(null)));
* oFileInfo = new FileInfo(folder + "bestNetwork" + j + ".xml");
* doc.Save(oFileInfo.FullName);*/
}
var msg = DateTime.Now.ToLongTimeString()
+ "; Duration=" + DateTime.Now.Subtract(dt).ToString("mm\\:ss")
+ "; Gen=" + ea.Generation.ToString("000") + "; Neurons=" + (ea.Population.TotalNeuronCount / (float)ea.Population.GenomeList.Count).ToString("00.00") + "; Connections=" + (ea.Population.TotalConnectionCount / (float)ea.Population.GenomeList.Count).ToString("00.00")
+ "; BestFit=" + ea.BestGenome.ObjectiveFitness.ToString("0.000") + "; MaxFit=" + maxFitness.ToString("0.000");
Console.WriteLine(msg);
logWriter.WriteLine(msg);
logWriter.Flush();
//Do any post-hoc stuff here
}
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome, ActivationFunctionFactory.GetActivationFunction("NullFn"));
oFileInfo = new FileInfo(folder + "bestGenome.xml");
doc.Save(oFileInfo.FullName);
}
public GeneticAlgorithmSettings(GenomeFactory genomeFactory)
{
mGenomeFactory = genomeFactory;
}
static void Main(string[] args)
{
string folder = "";
NeatGenome seedGenome = null;
string filename = null;
string shape = "triangle";
bool isMulti = false;
for (int j = 0; j < args.Length; j++)
{
if (j <= args.Length - 2)
{
switch (args[j])
{
case "-seed": filename = args[++j];
Console.WriteLine("Attempting to use seed from file " + filename);
break;
case "-folder": folder = args[++j];
Console.WriteLine("Attempting to output to folder " + folder);
break;
case "-shape": shape = args[++j];
Console.WriteLine("Attempting to do experiment with shape " + shape);
break;
case "-multi": isMulti = Boolean.Parse(args[++j]);
Console.WriteLine("Experiment is heterogeneous? " + isMulti);
break;
}
}
}
if (filename != null)
{
try
{
XmlDocument document = new XmlDocument();
document.Load(filename);
seedGenome = XmlNeatGenomeReaderStatic.Read(document);
}
catch (Exception e)
{
System.Console.WriteLine("Problem loading genome. \n" + e.Message);
}
}
double maxFitness = 0;
int maxGenerations = 1000;
int populationSize = 150;
int inputs = 4;
IExperiment exp = new SkirmishExperiment(inputs, 1, isMulti, shape);
StreamWriter SW;
SW = File.CreateText(folder + "logfile.txt");
XmlDocument doc;
FileInfo oFileInfo;
IdGenerator idgen;
EvolutionAlgorithm ea;
if (seedGenome == null)
{
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);
}
else
{
idgen = new IdGeneratorFactory().CreateIdGenerator(seedGenome);
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(seedGenome, populationSize, exp.DefaultNeatParameters, idgen)), 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(folder + "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(ea.Generation.ToString() + " " + (maxFitness).ToString());
}
SW.Close();
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome, ActivationFunctionFactory.GetActivationFunction("NullFn"));
oFileInfo = new FileInfo(folder + "bestGenome.xml");
doc.Save(oFileInfo.FullName);
}
/// <summary>
/// This function contains all of the pre-run logic that doesn't involve graphics.
/// </summary>
protected override void Initialize()
{
base.Initialize();
// Create the world / region system
// Note: The morphology must be generated in advance of the Load
INetwork morphologyCPPN = loadCPPNFromXml(initialMorphologyFilename).Decode(ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid"));
morphology = generateMorphology(morphologyCPPN);
redTexture = generateSolidMorphology(morphology);
InitializeRegions();
// Initialize a log to track some instance-specific data
using (System.IO.StreamWriter file = new System.IO.StreamWriter("RunInfo.txt", true))
{
file.WriteLine("Novelty search run");
file.WriteLine("Start time: " + DateTime.Now.ToString("HH:mm:ss tt"));
if (freezeAfterPlanting)
{
file.WriteLine("Individuals are immobilized once they attempt to plant.");
}
else
{
file.WriteLine("Individuals are allowed to keep moving even if/after they attempt to plant.");
}
file.WriteLine("Morphology genome XML filename: " + initialControllerFilename);
file.WriteLine("Behavior update interval: " + behaviorUpdateInterval);
file.WriteLine("Planting weight: " + plantingWeight);
file.WriteLine("Position weight: " + positionWeight);
file.WriteLine("Population size: " + populationSize);
file.WriteLine("Archive threshold: " + archiveThreshold);
}
// Initialize some static variables for the simulation
numBidirectionalPlanters = 0;
numFirstTrialPlanters = 0;
numSecondTrialPlanters = 0;
numBidirectionalMisplanters = 0;
numFirstTrialMisplanters = 0;
numSecondTrialMisplanters = 0;
firstTrial = true;
// Set the NEAT parameters
neatParams = new NeatParameters();
neatParams.archiveThreshold = archiveThreshold;
neatParams.noveltyFixed = true;
neatParams.noveltySearch = true;
// Configure the HyperNEAT substrate
controllerSubstrate = new ControllerSubstrate(308, 4, 108, new BipolarSigmoid());
controllerSubstrate.weightRange = 5.0;
controllerSubstrate.threshold = 0.2;
// Create a genome factory to generate a list of CPPN genomes
cppnGenerator = new GenomeFactory();
idGen = new IdGenerator();
cppnGenomeList = cppnGenerator.CreateGenomeList(neatParams, idGen, 4, 8, 1.0f, populationSize);
GenomeIndexOfCurrentCreature = 0;
// Initialize the folders for storing the archive and planters
noveltyLogsFolder = Directory.GetCurrentDirectory() + "\\archive\\" + GenomeIndexOfCurrentCreature + "\\";
if (!Directory.Exists(noveltyLogsFolder))
{
Directory.CreateDirectory(noveltyLogsFolder);
}
plantersFolder = Directory.GetCurrentDirectory() + "\\planters\\" + GenomeIndexOfCurrentCreature + "\\";
if (!Directory.Exists(plantersFolder))
{
Directory.CreateDirectory(plantersFolder);
}
// Create an initial population based on the genome list
popn = new Population(idGen, cppnGenomeList);
// Set the generation counter
// Note: This must be kept seperately from the EA generation counter because novelty search here does't follow the traditional loop.
generation = 1;
// Create the EA
// (Don't run the EA until the first generation has had a chance to go through the simulation.
// The EA call happens in Simulator.NewGeneration().)
ea = new EvolutionAlgorithm(popn, new ChromariaPopulationEvaluator(new ChromariaNetworkEvaluator()), neatParams);
// Initialize the behavior trackers for this individual
ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Behavior = new BehaviorType();
ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Behavior.behaviorList = new List <double>();
// Generate the initial creature
int x = initialBoardWidth / 2;
int y = initialBoardHeight / 2;
INetwork newController = controllerSubstrate.generateGenome(ea.Population.GenomeList[GenomeIndexOfCurrentCreature].Decode(ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid"))).Decode(ActivationFunctionFactory.GetActivationFunction("BipolarSigmoid"));
if (bidirectionalTrials)
{
currentCreature = new NNControlledCreature(morphology, x, y, initialHeading + (float)(Math.PI / 2.0), newController, this, drawSensorField, trackPlanting, defaultNumSensors, freezeAfterPlanting);
}
else
{
currentCreature = new NNControlledCreature(morphology, x, y, initialHeading, newController, this, drawSensorField, trackPlanting, defaultNumSensors, freezeAfterPlanting);
}
currentCreature.DrawOrder = 1;
indexOfCurrentCreature = Components.Count - 1;
// Add the creature to the simulator's region lists
int currentPointer = Components.Count - 1;
regions[y / regionHeight, x / regionWidth].Add(currentPointer);
if ((x % regionWidth > (x + morphology.Width) % regionWidth) && (y % regionHeight > (y + morphology.Height) % regionHeight) && !regions[(y + morphology.Height) / regionHeight, (x + morphology.Width) / regionWidth].Contains(currentPointer))
{
regions[(y + morphology.Height) / regionHeight, (x + morphology.Width) / regionWidth].Add(currentPointer);
}
if (x % regionWidth > (x + morphology.Width) % regionWidth && !regions[(y / regionHeight), (x + morphology.Width) / regionWidth].Contains(currentPointer))
{
regions[(y / regionHeight), (x + morphology.Width) / regionWidth].Add(currentPointer);
}
if (y % regionHeight > (y + morphology.Height) % regionHeight && !(regions[(y + morphology.Height) / regionHeight, x / regionWidth].Contains(currentPointer)))
{
regions[(y + morphology.Height) / regionHeight, x / regionWidth].Add(currentPointer);
}
// Preliminarily update the creature's sensors so its first movements are actually based on what's underneath its starting position
currentCreature.InitializeSensor();
plantedInColoredSpace1 = false;
plantedInColoredSpace2 = false;
plantedInWhiteSpace1 = false;
plantedInWhiteSpace2 = false;
numUpdates = 0;
}
private void startEvolutionToolStripMenuItem_Click(object sender, EventArgs e)
{
NeatGenome seedGenome = null;
if (EvoSeedFileName != null)
{
try
{
XmlDocument document = new XmlDocument();
document.Load(EvoSeedFileName);
seedGenome = XmlNeatGenomeReaderStatic.Read(document);
}
catch (Exception ex)
{
System.Console.WriteLine("Problem loading genome. \n" + ex.Message);
}
}
double maxFitness = 0;
int maxGenerations = 1000;
int populationSize = 150;
int inputs = 4;
IExperiment exp = new SkirmishExperiment(inputs, 1, EvoIsMulti, EvoShape);
w = SkirmishNetworkEvaluator.lWorldVar(network, 125);
timer = 0;
StreamWriter SW;
SW = File.CreateText(EvoOutputLogFolder + "logfile.txt");
XmlDocument doc;
FileInfo oFileInfo;
IdGenerator idgen;
EvolutionAlgorithm ea;
if (seedGenome == null)
{
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);
}
else
{
idgen = new IdGeneratorFactory().CreateIdGenerator(seedGenome);
ea = new EvolutionAlgorithm(new Population(idgen, GenomeFactory.CreateGenomeList(seedGenome, populationSize, exp.DefaultNeatParameters, idgen)), 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(EvoOutputLogFolder + "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(ea.Generation.ToString() + " " + (maxFitness).ToString());
}
SW.Close();
doc = new XmlDocument();
XmlGenomeWriterStatic.Write(doc, (NeatGenome)ea.BestGenome, ActivationFunctionFactory.GetActivationFunction("NullFn"));
oFileInfo = new FileInfo(EvoOutputLogFolder + "bestGenome.xml");
doc.Save(oFileInfo.FullName);
}
