/// <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>
/// <returns>Constructed evolutionary algorithm</returns>
public override INeatEvolutionAlgorithm<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, 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 MazeNavigationFitnessEvaluator(MaxDistanceToTarget, MaxTimesteps, MazeVariant,
MinSuccessDistance);
// Create genome decoder.
var genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeEvaluator<NeatGenome> fitnessEvaluator =
new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, mazeNavigationEvaluator,
ParallelOptions);
// Initialize the evolution algorithm.
ea.Initialize(fitnessEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return ea;
}
/// <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>
/// Create and return a GenerationalNeatEvolutionAlgorithm 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 genome population and their associated/parent genome factory.
/// </summary>
public INeatEvolutionAlgorithm<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, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
GenerationalNeatEvolutionAlgorithm<NeatGenome> ea = new GenerationalNeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
PreyCaptureEvaluator evaluator = new PreyCaptureEvaluator(_trialsPerEvaluation, _gridSize, _preyInitMoves, _preySpeed, _sensorRange, _maxTimesteps);
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// TODO: evaulation scheme that re-evaulates existing genomes and takes average over time.
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeEvaluator<NeatGenome> genomeFitnessEvaluator = new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);
// Initialize the evolution algorithm.
ea.Initialize(genomeFitnessEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return ea;
}
/// <summary>
/// Creates and returns a GenerationalNeatEvolutionAlgorithm 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 genome
/// population and their associated/parent genome factory.
/// </summary>
/// <param name="genomeFactory">The NEAT genome factory.</param>
/// <param name="genomeList">The initial list of genomes.</param>
/// <returns>The NEAT evolution algorithm.</returns>
public 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
IComplexityRegulationStrategy complexityRegulationStrategy =
ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Initialize the logger
if (_generationalLogFile != null)
{
logger =
new FileDataLogger(_generationalLogFile);
}
// Create the evolution algorithm
GenerationalNeatEvolutionAlgorithm<NeatGenome> ea =
new GenerationalNeatEvolutionAlgorithm<NeatGenome>(NeatEvolutionAlgorithmParameters, speciationStrategy,
complexityRegulationStrategy, logger);
// Create evalutor
BinaryEvolvedAutoencoderEvaluator evaluator = new BinaryEvolvedAutoencoderEvaluator(_trainingImagesFilename,
InputCount, _numImageSamples, _learningRate, _numBackpropIterations, _trainingSampleProportion);
// Create genome decoder
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator
IGenomeEvaluator<NeatGenome> innerFitnessEvaluator =
new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);
// 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 determined by examining each genome's evaluation info object.
IGenomeEvaluator<NeatGenome> selectiveFitnessEvaluator = new SelectiveGenomeFitnessEvaluator<NeatGenome>(
innerFitnessEvaluator,
SelectiveGenomeFitnessEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
// Initialize the evolution algorithm
ea.Initialize(selectiveFitnessEvaluator, genomeFactory, genomeList, null);
// 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 GenerationalNeatEvolutionAlgorithm<NeatGenome>(SpeciationStrategy,
ComplexityRegulationStrategy, RunPhase.Initialization);
// Create IBlackBox evaluator.
MazeNavigatorFitnessInitializationEvaluator mazeNavigatorEvaluator =
new MazeNavigatorFitnessInitializationEvaluator(MaxTimesteps, MinSuccessDistance, MaxDistanceToTarget,
mazeEnvironment, startingEvaluations);
// Create the genome evaluator
IGenomeEvaluator<NeatGenome> fitnessEvaluator = new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(
genomeDecoder, mazeNavigatorEvaluator, parallelOptions);
// 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);
}
/// <summary>
/// Create and return a GenerationalNeatEvolutionAlgorithm 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 genome population and their associated/parent genome factory.
/// </summary>
public INeatEvolutionAlgorithm<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, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
GenerationalNeatEvolutionAlgorithm<NeatGenome> ea = new GenerationalNeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
SinglePoleBalancingBox2dEvaluator evaluator = new SinglePoleBalancingBox2dEvaluator();
// Create genome decoder.
IGenomeDecoder<NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeEvaluator<NeatGenome> innerFitnessEvaluator = new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);
// 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.
IGenomeEvaluator<NeatGenome> selectiveFitnessEvaluator = new SelectiveGenomeFitnessEvaluator<NeatGenome>(
innerFitnessEvaluator,
SelectiveGenomeFitnessEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveFitnessEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return ea;
}
/// <summary>
/// Create and return a GenerationalNeatEvolutionAlgorithm 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 genome population and their associated/parent genome factory.
/// </summary>
public INeatEvolutionAlgorithm<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, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
GenerationalNeatEvolutionAlgorithm<NeatGenome> ea = new GenerationalNeatEvolutionAlgorithm<NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
EvolvedAutoencoderEvaluator evaluator = new EvolvedAutoencoderEvaluator(_trainingImagesFilename,
_visualFieldPixelCount, _numImageSamples, _learningRate, _numBackpropIterations, _trainingSampleProportion, _resolutionReductionPerSide);
// Create genome decoder. Decodes to a neural network packaged with an activation scheme that defines a fixed number of activations per evaluation.
IGenomeDecoder<NeatGenome,IBlackBox> genomeDecoder = CreateGenomeDecoder(_visualFieldResolution/ _resolutionReductionPerSide, _lengthCppnInput);
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeEvaluator<NeatGenome> innerFitnessEvaluator = new ParallelGenomeFitnessEvaluator<NeatGenome, IBlackBox>(genomeDecoder, evaluator, _parallelOptions);
// 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.
IGenomeEvaluator<NeatGenome> selectiveFitnessEvaluator = new SelectiveGenomeFitnessEvaluator<NeatGenome>(
innerFitnessEvaluator,
SelectiveGenomeFitnessEvaluator<NeatGenome>.CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveFitnessEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return ea;
}
