/// <summary>
/// Create and return a SteadyStateNeatEvolutionAlgorithm object (specific to fitness-based evaluations) ready for
/// running the
/// NEAT algorithm/search based on the given genome factory and genome list. Various sub-parts of the algorithm are
/// also constructed and connected up.
/// </summary>
/// <param name="genomeFactory">The genome factory from which to generate new genomes</param>
/// <param name="genomeList">The current genome population</param>
/// <param name="startingEvaluations">The number of evaluations that have been executed prior to the current run.</param>
/// <returns>Constructed evolutionary algorithm</returns>
public override INeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(
IGenomeFactory<NeatGenome> genomeFactory,
List<NeatGenome> genomeList, ulong startingEvaluations)
{
ulong initializationEvaluations;
// Instantiate the internal initialization algorithm
_mazeNavigationInitializer.InitializeAlgorithm(ParallelOptions, genomeList,
CreateGenomeDecoder(), startingEvaluations);
// Run the initialization algorithm until the requested number of viable seed genomes are found
List<NeatGenome> seedPopulation = _mazeNavigationInitializer.EvolveViableGenomes(SeedGenomeCount, true,
out initializationEvaluations);
// Create complexity regulation strategy.
var complexityRegulationStrategy =
ExperimentUtils.CreateComplexityRegulationStrategy(ComplexityRegulationStrategy, Complexitythreshold);
// Create the evolution algorithm.
AbstractNeatEvolutionAlgorithm<NeatGenome> ea =
new QueueingNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
new ParallelKMeansClusteringStrategy<NeatGenome>(new ManhattanDistanceMetric(1.0, 0.0, 10.0),
ParallelOptions),
complexityRegulationStrategy, _batchSize, RunPhase.Primary, (_bridgingMagnitude > 0),
false, _evolutionDataLogger, _experimentLogFieldEnableMap);
// Create IBlackBox evaluator.
IPhenomeEvaluator<IBlackBox, BehaviorInfo> mazeNavigationEvaluator =
new MazeNavigationMCSEvaluator(MaxDistanceToTarget, MaxTimesteps,
MazeVariant, MinSuccessDistance, _behaviorCharacterizationFactory,
_bridgingMagnitude, _bridgingApplications, initializationEvaluations);
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// IGenomeEvaluator<NeatGenome> fitnessEvaluator =
// new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
// SelectionType.Queueing, SearchType.MinimalCriteriaSearch, _evaluationDataLogger,
// SerializeGenomeToXml);
IGenomeEvaluator<NeatGenome> fitnessEvaluator =
new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
SelectionType.Queueing, SearchType.MinimalCriteriaSearch, _evaluationDataLogger,
SerializeGenomeToXml);
// Initialize the evolution algorithm.
ea.Initialize(fitnessEvaluator, genomeFactory, seedPopulation, DefaultPopulationSize,
null, MaxEvaluations + startingEvaluations);
// Finished. Return the evolution algorithm
return ea;
}
/// <summary>
/// Create and return a SteadyStateNeatEvolutionAlgorithm object (specific to fitness-based evaluations) ready for
/// running the
/// NEAT algorithm/search based on the given genome factory and genome list. Various sub-parts of the algorithm are
/// also constructed and connected up.
/// </summary>
/// <param name="genomeFactory">The genome factory from which to generate new genomes</param>
/// <param name="genomeList">The current genome population</param>
/// <param name="startingEvaluations">The number of evaluations that have been executed prior to the current run.</param>
/// <returns>Constructed evolutionary algorithm</returns>
public override INeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(
IGenomeFactory<NeatGenome> genomeFactory,
List<NeatGenome> genomeList, ulong startingEvaluations)
{
// 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.
var complexityRegulationStrategy =
ExperimentUtils.CreateComplexityRegulationStrategy(ComplexityRegulationStrategy, Complexitythreshold);
// Create the evolution algorithm.
AbstractNeatEvolutionAlgorithm<NeatGenome> ea =
new SteadyStateNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
speciationStrategy, complexityRegulationStrategy, _batchSize, _populationEvaluationFrequency,
RunPhase.Primary, _evolutionDataLogger);
// Create IBlackBox evaluator.
MazeNavigationMCSEvaluator mazeNavigationEvaluator = new MazeNavigationMCSEvaluator(MaxDistanceToTarget,
MaxTimesteps,
MazeVariant,
MinSuccessDistance, _behaviorCharacterizationFactory);
// Create genome decoder.
var genomeDecoder = CreateGenomeDecoder();
// IGenomeEvaluator<NeatGenome> fitnessEvaluator =
// new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
// SearchType.MinimalCriteriaSearch);
IGenomeEvaluator<NeatGenome> fitnessEvaluator =
new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
SelectionType.SteadyState, SearchType.MinimalCriteriaSearch, _evaluationDataLogger);
// Initialize the evolution algorithm.
ea.Initialize(fitnessEvaluator, genomeFactory, genomeList, null, MaxEvaluations);
// Finished. Return the evolution algorithm
return ea;
}
