/// <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 genome population and their associated/parent genome 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, _parallelOptions);
// Create complexity regulation strategy.
IComplexityRegulationStrategy complexityRegulationStrategy = ExperimentUtils.CreateComplexityRegulationStrategy(_complexityRegulationStr, _complexityThreshold);
// Create the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, complexityRegulationStrategy);
// Create IBlackBox evaluator.
FunctionRegressionEvaluator evaluator = new FunctionRegressionEvaluator(_paramSamplingInfoArr, _func);
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
// Create a genome list evaluator. This packages up the genome decoder with the genome evaluator.
IGenomeListEvaluator <NeatGenome> innerEvaluator = new ParallelGenomeListEvaluator <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.
IGenomeListEvaluator <NeatGenome> selectiveEvaluator = new SelectiveGenomeListEvaluator <NeatGenome>(
innerEvaluator,
SelectiveGenomeListEvaluator <NeatGenome> .CreatePredicate_OnceOnly());
// Initialize the evolution algorithm.
ea.Initialize(selectiveEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(ea);
}
/// <summary>
/// Initialize the experiment with some optional XML configutation data.
/// </summary>
public void Initialize(string name, XmlElement xmlConfig)
{
_name = name;
_populationSize = XmlUtils.GetValueAsInt(xmlConfig, "PopulationSize");
_specieCount = XmlUtils.GetValueAsInt(xmlConfig, "SpecieCount");
_activationScheme = ExperimentUtils.CreateActivationScheme(xmlConfig, "Activation");
_complexityRegulationStr = XmlUtils.TryGetValueAsString(xmlConfig, "ComplexityRegulationStrategy");
_complexityThreshold = XmlUtils.TryGetValueAsInt(xmlConfig, "ComplexityThreshold");
_description = XmlUtils.TryGetValueAsString(xmlConfig, "Description");
_parallelOptions = ExperimentUtils.ReadParallelOptions(xmlConfig);
_eaParams = new NeatEvolutionAlgorithmParameters();
_eaParams.SpecieCount = _specieCount;
_neatGenomeParams = new NeatGenomeParameters();
_neatGenomeParams.FeedforwardOnly = _activationScheme.AcyclicNetwork;
// Determne what function to regress.
string fnIdStr = XmlUtils.GetValueAsString(xmlConfig, "Function");
FunctionId fnId = (FunctionId)Enum.Parse(typeof(FunctionId), fnIdStr);
_func = FunctionRegressionEvaluator.GetFunction(fnId);
// Read parameter sampling scheme settings.
int sampleResolution = XmlUtils.GetValueAsInt(xmlConfig, "SampleResolution");
double sampleMin = XmlUtils.GetValueAsDouble(xmlConfig, "SampleMin");
double sampleMax = XmlUtils.GetValueAsDouble(xmlConfig, "SampleMax");
int paramCount = _func.InputCount;
_paramSamplingInfoArr = new ParameterSamplingInfo[paramCount];
for (int i = 0; i < paramCount; i++)
{
_paramSamplingInfoArr[i] = new ParameterSamplingInfo(sampleMin, sampleMax, sampleResolution);
}
}
