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;
}
/// <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>(EvoParameters, speciationStrategy, complexityRegulationStrategy);
// Create a genome list evaluator. This packages up the genome decoder with the phenome evaluator.
if(Evaluator == null)
Evaluator = CreateEvaluator();
// Initialize the evolution algorithm.
ea.Initialize(Evaluator, genomeFactory, genomeList);
// 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;
}
