private NeatEvolutionAlgorithm <NeatGenome> GenerateTeam()
{
NeatEvolutionAlgorithmParameters neatParams = new NeatEvolutionAlgorithmParameters();
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(0.4, 1.0, 0.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric);
IComplexityRegulationStrategy complexityStrategy = new NullComplexityRegulationStrategy();
return(new NeatEvolutionAlgorithm <NeatGenome>(neatParams, speciationStrategy, complexityStrategy));
}
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList, IGenomeListEvaluator <NeatGenome> eval = null)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = new NullComplexityRegulationStrategy();// ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create the MC evaluator
PasswordCrackingEvaluator.Passwords = _passwords;
// Create genome decoder.
IGenomeDecoder <NeatGenome, MarkovChain> genomeDecoder = CreateGenomeDecoder();
// If we're running specially on Condor, skip this
if (eval == null)
{
_evaluator = new PasswordCrackingEvaluator(_guesses, Hashed);
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
// IGenomeListEvaluator<NeatGenome> innerEvaluator = new ParallelGenomeListEvaluator<NeatGenome, MarkovChain>(genomeDecoder, _evaluator, _parallelOptions);
IGenomeListEvaluator <NeatGenome> innerEvaluator = new ParallelNEATGenomeListEvaluator <NeatGenome, MarkovChain>(genomeDecoder, _evaluator, this);
/*
* // Wrap the list evaluator in a 'selective' evaulator that will only evaluate new genomes. That is, we skip re-evaluating any genomes
* // that were in the population in previous generations (elite genomes). This is determiend by examining each genome's evaluation info object.
* IGenomeListEvaluator<NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator<NeatGenome>(
* innerEvaluator,
* SelectiveGenomeListEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
*/
// Initialize the evolution algorithm.
ea.Initialize(innerEvaluator, genomeFactory, genomeList);
}
else
{
// Initialize the evolution algorithm.
ea.Initialize(eval, genomeFactory, genomeList);
}
// Finished. Return the evolution algorithm
return(ea);
}
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(
IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10;
// for matched genes the distance is their weight difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy =
new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = new NullComplexityRegulationStrategy();
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea =
new NeatEvolutionAlgorithm <NeatGenome>(this.eaParams, speciationStrategy, complexityRegulationStrategy);
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = this.CreateGenomeDecoder();
var evaluator = new Evaluator();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
//IGenomeListEvaluator<NeatGenome> innerEvaluator =
// new SerialGenomeListEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator);
IGenomeListEvaluator <NeatGenome> innerEvaluator =
new ParallelGenomeListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, evaluator);
// Wrap the list evaluator in a 'selective' evaluator that will only evaluate new genomes.
// That is, we skip re-evaluating any genomes that were in the population in previous
// generations (elite genomes). This is determined by examining each genome's evaluation info object.
IGenomeListEvaluator <NeatGenome> selectiveEvaluator =
new SelectiveGenomeListEvaluator <NeatGenome>(
innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
ea.UpdateScheme = new UpdateScheme(1);
// Finished. Return the evolution algorithm
return(ea);
}
/// <summary>
/// Create and return a NeatEvolutionAlgorithm object ready for running the NEAT algorithm/search. Various sub-parts
/// of the algorithm are also constructed and connected up.
/// This overload accepts a pre-built genome2 population and their associated/parent genome2 factory.
/// </summary>
public NeatEvolutionAlgorithm <NeatGenome> CreateEvolutionAlgorithm(IGenomeFactory <NeatGenome> genomeFactory, List <NeatGenome> genomeList)
{
// Create distance metric. Mismatched genes have a fixed distance of 10; for matched genes the distance is their weigth difference.
IDistanceMetric distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
ISpeciationStrategy <NeatGenome> speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, new ParallelOptions());
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = new NullComplexityRegulationStrategy();// ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the phenome evaluator.
_evaluator = new ForagingEvaluator <NeatGenome>(genomeDecoder, _world, _agentType, _navigationEnabled, _hidingEnabled)
{
MaxTimeSteps = _timeStepsPerGeneration,
EvoParadigm = _paradigm,
MemParadigm = _memory,
GenerationsPerMemorySize = _memGens,
MaxMemorySize = _maxMemorySize,
TeachParadigm = _teaching,
TrialId = TrialId,
PredatorCount = _predCount,
PredatorDistribution = PredatorDistribution,
PredatorTypes = _predTypes,
PredatorGenerations = _predGens,
DistinguishPredators = _distinguishPreds,
LogDiversity = _logDiversity
};
// Initialize the evolution algorithm.
ea.Initialize(_evaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(ea);
}
private static void Train()
{
File.WriteAllText($"{NeatConsts.experimentName}/fitness.csv", "generation,firness\n");
var neatGenomeFactory = new NeatGenomeFactory(NeatConsts.ViewX * NeatConsts.ViewY * NeatConsts.typeIds.Count, 1);
var genomeList = neatGenomeFactory.CreateGenomeList(NeatConsts.SpecCount, 0);
var eaParams = new NeatEvolutionAlgorithmParameters
{
SpecieCount = NeatConsts.SpecCount
};
//var distanceMetric = new ManhattanDistanceMetric(1.0, 0.0, 10.0);
var distanceMetric = new ManhattanDistanceMetric();
var parallelOptions = new ParallelOptions();
var speciationStrategy = new ParallelKMeansClusteringStrategy <NeatGenome>(distanceMetric, parallelOptions);
//var speciationStrategy = new KMeansClusteringStrategy<NeatGenome>(distanceMetric);
//var speciationStrategy = new RandomClusteringStrategy<NeatGenome>();
var complexityRegulationStrategy = new NullComplexityRegulationStrategy();
//var complexityRegulationStrategy = new DefaultComplexityRegulationStrategy(ComplexityCeilingType.Relative, 0.50);
var ea = new NeatEvolutionAlgorithm <NeatGenome>(eaParams, speciationStrategy, complexityRegulationStrategy);
var activationScheme = NetworkActivationScheme.CreateCyclicFixedTimestepsScheme(1);
var genomeDecoder = new NeatGenomeDecoder(activationScheme);
var phenomeEvaluator = new GameEvaluator();
var genomeListEvaluator = new ParallelGenomeListEvaluator <NeatGenome, IBlackBox>(genomeDecoder, phenomeEvaluator, parallelOptions);
ea.Initialize(genomeListEvaluator, neatGenomeFactory, genomeList);
ea.UpdateScheme = new UpdateScheme(NeatConsts.LogRate);
ea.StartContinue();
ea.UpdateEvent += Ea_UpdateEvent;
while (ea.RunState != RunState.Paused)
{
}
ea.Stop();
}