/// <summary>
/// Create and return a GenerationalNeatEvolutionAlgorithm 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.
var ea = new GenerationalNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
speciationStrategy,
complexityRegulationStrategy);
// Create IBlackBox evaluator.
var mazeNavigationEvaluator = new MazeNavigationNoveltyEvaluator(MaxDistanceToTarget, MaxTimesteps,
MazeVariant,
MinSuccessDistance, _behaviorCharacterizationFactory);
// Create genome decoder.
var genomeDecoder = CreateGenomeDecoder();
// Create a novelty archive.
AbstractNoveltyArchive<NeatGenome> archive =
new BehavioralNoveltyArchive<NeatGenome>(_archiveAdditionThreshold,
_archiveThresholdDecreaseMultiplier, _archiveThresholdIncreaseMultiplier,
_maxGenerationArchiveAddition, _maxGenerationsWithoutArchiveAddition);
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
// IGenomeFitnessEvaluator<NeatGenome> fitnessEvaluator =
// new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator, _nearestNeighbors, archive);
IGenomeEvaluator<NeatGenome> fitnessEvaluator =
new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
SelectionType.Generational, SearchType.NoveltySearch,
ParallelOptions, _nearestNeighbors, archive);
// Initialize the evolution algorithm.
ea.Initialize(fitnessEvaluator, genomeFactory, genomeList, MaxGenerations, null, archive);
// Finished. Return the evolution algorithm
return ea;
}
/// <summary>
/// Configures and instantiates the initialization evolutionary algorithm.
/// </summary>
/// <param name="parallelOptions">Synchronous/Asynchronous execution settings.</param>
/// <param name="genomeList">The initial population of genomes.</param>
/// <param name="genomeFactory">The genome factory initialized by the main evolution thread.</param>
/// <param name="mazeEnvironment">The maze on which to evaluate the navigators.</param>
/// <param name="genomeDecoder">The decoder to translate genomes into phenotypes.</param>
/// <param name="startingEvaluations">
/// The number of evaluations that preceeded this from which this process will pick up
/// (this is used in the case where we're restarting a run because it failed to find a solution in the allotted time).
/// </param>
public override void InitializeAlgorithm(ParallelOptions parallelOptions, List <NeatGenome> genomeList,
IGenomeFactory <NeatGenome> genomeFactory, MazeStructure mazeEnvironment,
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder, ulong startingEvaluations)
{
// Set the boiler plate algorithm parameters
base.InitializeAlgorithm(parallelOptions, genomeList, genomeDecoder, startingEvaluations);
// Create the initialization evolution algorithm.
InitializationEa = new SteadyStateComplexifyingEvolutionAlgorithm <NeatGenome>(EvolutionAlgorithmParameters,
SpeciationStrategy, ComplexityRegulationStrategy, _batchSize, _populationEvaluationFrequency,
RunPhase.Initialization, NavigatorEvolutionDataLogger, NavigatorEvolutionLogFieldEnableMap,
NavigatorPopulationDataLogger, PopulationLoggingBatchInterval);
// Create IBlackBox evaluator.
MazeNavigatorNoveltySearchInitializationEvaluator mazeNavigatorEvaluator =
new MazeNavigatorNoveltySearchInitializationEvaluator(MinSuccessDistance,
MaxDistanceToTarget, mazeEnvironment, _behaviorCharacterizationFactory, startingEvaluations);
// Create a novelty archive
AbstractNoveltyArchive <NeatGenome> archive =
new BehavioralNoveltyArchive <NeatGenome>(_archiveAdditionThreshold,
_archiveThresholdDecreaseMultiplier, _archiveThresholdIncreaseMultiplier,
_maxGenerationArchiveAddition, _maxGenerationsWithoutArchiveAddition);
// Create the genome evaluator
IGenomeEvaluator <NeatGenome> fitnessEvaluator =
new ParallelGenomeBehaviorEvaluator <NeatGenome, IBlackBox>(genomeDecoder, mazeNavigatorEvaluator,
SearchType.NoveltySearch, _nearestNeighbors);
// Only pull the number of genomes from the list equivalent to the initialization algorithm population size
// (this is to handle the case where the list was created in accordance with the primary algorithm
// population size, which is quite likely larger)
genomeList = genomeList.Take(PopulationSize).ToList();
// Replace genome factory primary NEAT parameters with initialization parameters
((NeatGenomeFactory)genomeFactory).ResetNeatGenomeParameters(NeatGenomeParameters);
// Initialize the evolution algorithm
InitializationEa.Initialize(fitnessEvaluator, genomeFactory, genomeList, null, null, archive);
}
/// <summary>
/// Configures and instantiates the initialization evolutionary algorithm.
/// </summary>
/// <param name="parallelOptions">Synchronous/Asynchronous execution settings.</param>
/// <param name="genomeList">The initial population of genomes.</param>
/// <param name="genomeFactory">The genome factory initialized by the main evolution thread.</param>
/// <param name="genomeDecoder">The decoder to translate genomes into phenotypes.</param>
/// <param name="startingEvaluations">
/// The number of evaluations that preceeded this from which this process will pick up
/// (this is used in the case where we're restarting a run because it failed to find a solution in the allotted time).
/// </param>
public void InitializeAlgorithm(ParallelOptions parallelOptions, List<NeatGenome> genomeList,
IGenomeFactory<NeatGenome> genomeFactory, IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder, ulong startingEvaluations)
{
// Set the boiler plate algorithm parameters
base.InitializeAlgorithm(parallelOptions, genomeList, genomeDecoder, startingEvaluations);
// Create the initialization evolution algorithm.
InitializationEa = new SteadyStateNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
SpeciationStrategy, ComplexityRegulationStrategy, _batchSize, _populationEvaluationFrequency,
RunPhase.Initialization, _evolutionDataLogger, _initializationLogFieldEnableMap);
// Create IBlackBox evaluator.
MazeNavigationMCSInitializationEvaluator mazeNavigationEvaluator =
new MazeNavigationMCSInitializationEvaluator(MaxDistanceToTarget, MaxTimesteps,
_mazeVariant,
MinSuccessDistance, _behaviorCharacterizationFactory, startingEvaluations);
// Create a novelty archive.
AbstractNoveltyArchive<NeatGenome> archive =
new BehavioralNoveltyArchive<NeatGenome>(_archiveAdditionThreshold,
_archiveThresholdDecreaseMultiplier, _archiveThresholdIncreaseMultiplier,
_maxGenerationArchiveAddition, _maxGenerationsWithoutArchiveAddition);
// IGenomeEvaluator<NeatGenome> fitnessEvaluator =
// new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
// SelectionType.SteadyState, SearchType.NoveltySearch,
// _nearestNeighbors, archive, _evaluationDataLogger, _serializeGenomeToXml);
IGenomeEvaluator<NeatGenome> fitnessEvaluator =
new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
SelectionType.SteadyState, SearchType.NoveltySearch,
_nearestNeighbors, archive, _evaluationDataLogger, _serializeGenomeToXml);
// Only pull the number of genomes from the list equivalent to the initialization algorithm population size
// (this is to handle the case where the list was created in accordance with the primary algorithm
// population size, which could have been larger)
genomeList = genomeList.Take(PopulationSize).ToList();
// Replace genome factory primary NEAT parameters with initialization parameters
((NeatGenomeFactory)genomeFactory).ResetNeatGenomeParameters(NeatGenomeParameters);
// Initialize the evolution algorithm.
InitializationEa.Initialize(fitnessEvaluator, genomeFactory, genomeList, PopulationSize, null,
_maxEvaluations + startingEvaluations,
archive);
}
/// <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>
/// <returns>Constructed evolutionary algorithm</returns>
public override INeatEvolutionAlgorithm<NeatGenome> CreateEvolutionAlgorithm(
IGenomeFactory<NeatGenome> genomeFactory,
List<NeatGenome> genomeList)
{
FileDataLogger logger = 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.
var complexityRegulationStrategy =
ExperimentUtils.CreateComplexityRegulationStrategy(ComplexityRegulationStrategy, Complexitythreshold);
// Initialize the logger
if (_generationalLogFile != null)
{
logger =
new FileDataLogger(_generationalLogFile);
}
// Create the evolution algorithm.
var ea = new SteadyStateNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters,
speciationStrategy, complexityRegulationStrategy, _batchSize, _populationEvaluationFrequency, logger);
// Create IBlackBox evaluator.
var mazeNavigationEvaluator = new MazeNavigationNoveltyEvaluator(MaxDistanceToTarget, MaxTimesteps,
MazeVariant,
MinSuccessDistance, _behaviorCharacterization);
// Create genome decoder.
var genomeDecoder = CreateGenomeDecoder();
// Create a novelty archive.
AbstractNoveltyArchive<NeatGenome> archive =
new BehavioralNoveltyArchive<NeatGenome>(_archiveAdditionThreshold,
_archiveThresholdDecreaseMultiplier, _archiveThresholdIncreaseMultiplier,
_maxGenerationArchiveAddition, _maxGenerationsWithoutArchiveAddition);
// IGenomeEvaluator<NeatGenome> fitnessEvaluator =
// new SerialGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
// _nearestNeighbors, archive);
IGenomeEvaluator<NeatGenome> fitnessEvaluator =
new ParallelGenomeBehaviorEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
_nearestNeighbors, archive);
// Initialize the evolution algorithm.
ea.Initialize(fitnessEvaluator, genomeFactory, genomeList, archive);
// Finished. Return the evolution algorithm
return ea;
}
