/// <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;
}
/// <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
EvolvedAutoencoderEvaluator evaluator = new EvolvedAutoencoderEvaluator(_trainingImagesFilename,
InputCount, _numImageSamples, _learningRate, _numBackpropIterations, _trainingSampleProportion, _resolutionReductionPerSide);
// 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);
// Finished. Return the evolution algorithm
return ea;
}
