public MultiObjectiveListEvaluator(IGenomeDecoder <TGenome, TPhenome> genomeDecoder,
IMultiObjectiveEvaluator <TPhenome> phenomeEvaluator,
INoveltyScorer <TGenome> noveltyScorer,
IGeneticDiversityScorer <TGenome> geneticDiversityScorer,
IMultiObjectiveScorer multiObjectiveScorer,
bool enableMultiThreading,
ParallelOptions options)
{
_genomeDecoder = genomeDecoder;
_phenomeEvaluator = phenomeEvaluator;
_noveltyScorer = noveltyScorer;
_geneticDiversityScorer = geneticDiversityScorer;
_multiObjectiveScorer = multiObjectiveScorer;
_parallelOptions = options;
_generation = 0;
// Determine the appropriate evaluation method.
if (enableMultiThreading)
{
_evalMethod = EvaluateParallel;
}
else
{
_evalMethod = EvaluateSerial;
}
}
public MultiObjectiveListEvaluator(IGenomeDecoder <TGenome, TPhenome> genomeDecoder,
IMultiObjectiveEvaluator <TPhenome> phenomeEvaluator,
INoveltyScorer <TGenome> noveltyScorer,
IList <IObjectiveScorer <TGenome> > objectiveScorers,
IMultiObjectiveScorer multiObjectiveScorer,
bool enableMultiThreading,
ParallelOptions options)
{
_genomeDecoder = genomeDecoder;
_phenomeEvaluator = phenomeEvaluator;
_noveltyScorer = noveltyScorer;
_objectiveScorers = objectiveScorers;
for (int i = 0; i < _objectiveScorers.Count; i++)
{
_objectiveScorers[i].Objective = i + 1;
}
_multiObjectiveScorer = multiObjectiveScorer;
_parallelOptions = options;
_generation = 0;
// Determine the appropriate evaluation method.
if (enableMultiThreading)
{
_evalMethod = EvaluateParallel;
}
else
{
_evalMethod = EvaluateSerial;
}
}
/// <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 the evolution algorithm.
NeatEvolutionAlgorithm <NeatGenome> ea = new NeatEvolutionAlgorithm <NeatGenome>(_eaParams, speciationStrategy, _complexityRegulationStrategy);
// Create genome decoder.
IGenomeDecoder <NeatGenome, IBlackBox> genomeDecoder = CreateGenomeDecoder();
bool noveltySearchEnabled = _noveltySearchParams?.Enabled ?? false;
bool multiObjectiveEnabled = _multiObjectiveParams?.Enabled ?? false;
INoveltyScorer <NeatGenome> noveltyScorer = null;
IMultiObjectiveScorer multiObjectiveScorer = null;
IList <IObjectiveScorer <NeatGenome> > objectiveScorers = new List <IObjectiveScorer <NeatGenome> >();
if (noveltySearchEnabled)
{
noveltyScorer = new TuringNoveltyScorer <NeatGenome>(_noveltySearchParams);
}
if (multiObjectiveEnabled)
{
multiObjectiveScorer = new NSGAII(_multiObjectiveParams);
if (NoveltySearchEnabled)
{
objectiveScorers.Add(noveltyScorer);
}
if (_multiObjectiveParams.GeneticDiversityEnabled)
{
objectiveScorers.Add(new GeneticDiversityKnn <NeatGenome>(_neatGenomeParams.ConnectionWeightRange));
}
if (_multiObjectiveParams.TapeSizeCostEnabled)
{
objectiveScorers.Add(new TapeSizeCostScorer <NeatGenome>());
}
if (_multiObjectiveParams.ConnectionCostEnabled)
{
objectiveScorers.Add(new ConnectionCostScorer <NeatGenome>());
}
}
_listEvaluator = new MultiObjectiveListEvaluator <NeatGenome, IBlackBox>(
genomeDecoder,
_evaluator,
noveltyScorer,
objectiveScorers,
multiObjectiveScorer,
_multiThreading,
_parallelOptions);
_listEvaluator.MultiObjectiveParams = _multiObjectiveParams;
_listEvaluator.ReportInterval = _logInterval;
NoveltySearchEnabled = noveltySearchEnabled;
MultiObjectiveEnabled = multiObjectiveEnabled;
// Initialize the evolution algorithm.
ea.Initialize(_listEvaluator, genomeFactory, genomeList);
// Finished. Return the evolution algorithm
return(ea);
}
