Class NSGAII<T, TInput, TOutput>

Namespace

AiDotNet.Genetics

Assembly

AiDotNet.dll

public class NSGAII<T, TInput, TOutput> : StandardGeneticAlgorithm<T, TInput, TOutput>, IGeneticAlgorithm<T, TInput, TOutput, ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, ModelParameterGene<T>>

Type Parameters

T

TInput

TOutput

Inheritance

object

GeneticBase<T, TInput, TOutput>

StandardGeneticAlgorithm<T, TInput, TOutput>

NSGAII<T, TInput, TOutput>

Implements

IGeneticAlgorithm<T, TInput, TOutput, ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, ModelParameterGene<T>>

Inherited Members

StandardGeneticAlgorithm<T, TInput, TOutput>.MutateGene(ModelParameterGene<T>)

StandardGeneticAlgorithm<T, TInput, TOutput>.MutateGeneGaussian(ModelParameterGene<T>)

StandardGeneticAlgorithm<T, TInput, TOutput>.InitializePopulation(int, InitializationMethod)

StandardGeneticAlgorithm<T, TInput, TOutput>.CreateIndividual(ICollection<ModelParameterGene<T>>)

StandardGeneticAlgorithm<T, TInput, TOutput>.IndividualToModel(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>)

StandardGeneticAlgorithm<T, TInput, TOutput>.GetMetaData()

StandardGeneticAlgorithm<T, TInput, TOutput>.SerializeIndividual(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>)

StandardGeneticAlgorithm<T, TInput, TOutput>.DeserializeIndividual(byte[])

StandardGeneticAlgorithm<T, TInput, TOutput>.SerializeModelData()

StandardGeneticAlgorithm<T, TInput, TOutput>.DeserializeModelData(byte[])

StandardGeneticAlgorithm<T, TInput, TOutput>.SerializePopulation()

StandardGeneticAlgorithm<T, TInput, TOutput>.DeserializePopulation(byte[])

GeneticBase<T, TInput, TOutput>.Population

GeneticBase<T, TInput, TOutput>.BestIndividual

GeneticBase<T, TInput, TOutput>.GeneticParams

GeneticBase<T, TInput, TOutput>.FitnessCalculator

GeneticBase<T, TInput, TOutput>.ModelEvaluator

GeneticBase<T, TInput, TOutput>.Random

GeneticBase<T, TInput, TOutput>.CrossoverOperators

GeneticBase<T, TInput, TOutput>.MutationOperators

GeneticBase<T, TInput, TOutput>.EvolutionStopwatch

GeneticBase<T, TInput, TOutput>.CurrentStats

GeneticBase<T, TInput, TOutput>.NumOps

GeneticBase<T, TInput, TOutput>.TrainingInputForInitialization

GeneticBase<T, TInput, TOutput>.AddDefaultCrossoverOperators()

GeneticBase<T, TInput, TOutput>.AddDefaultMutationOperators()

GeneticBase<T, TInput, TOutput>.MutateGene(ModelParameterGene<T>)

GeneticBase<T, TInput, TOutput>.MutateGeneGaussian(ModelParameterGene<T>)

GeneticBase<T, TInput, TOutput>.GetFitnessCalculator()

GeneticBase<T, TInput, TOutput>.SetFitnessCalculator(IFitnessCalculator<T, TInput, TOutput>)

GeneticBase<T, TInput, TOutput>.GetPopulation()

GeneticBase<T, TInput, TOutput>.GetBestIndividual()

GeneticBase<T, TInput, TOutput>.EvaluateIndividual(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, TInput, TOutput, TInput, TOutput)

GeneticBase<T, TInput, TOutput>.Evolve(int, TInput, TOutput, TInput, TOutput, Func<EvolutionStats<T, TInput, TOutput>, bool>)

GeneticBase<T, TInput, TOutput>.CreateNextGeneration(TInput, TOutput, TInput, TOutput)

GeneticBase<T, TInput, TOutput>.GetElites(int)

GeneticBase<T, TInput, TOutput>.EvaluatePopulation(TInput, TOutput, TInput, TOutput)

GeneticBase<T, TInput, TOutput>.UpdateEvolutionStats()

GeneticBase<T, TInput, TOutput>.FindBestIndividual()

GeneticBase<T, TInput, TOutput>.CalculateDiversity()

GeneticBase<T, TInput, TOutput>.CalculateGeneticDistance(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>)

GeneticBase<T, TInput, TOutput>.InvertFitness(T)

GeneticBase<T, TInput, TOutput>.IsBetterFitness(T, T)

GeneticBase<T, TInput, TOutput>.Crossover(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, double)

GeneticBase<T, TInput, TOutput>.Mutate(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, double)

GeneticBase<T, TInput, TOutput>.Select(int, SelectionMethod)

GeneticBase<T, TInput, TOutput>.TruncationSelection(int)

GeneticBase<T, TInput, TOutput>.UniformSelection(int)

GeneticBase<T, TInput, TOutput>.StochasticUniversalSamplingSelection(int)

GeneticBase<T, TInput, TOutput>.TournamentSelection(int)

GeneticBase<T, TInput, TOutput>.RouletteWheelSelection(int)

GeneticBase<T, TInput, TOutput>.RankSelection(int)

GeneticBase<T, TInput, TOutput>.InitializePopulation(int, InitializationMethod)

GeneticBase<T, TInput, TOutput>.CreateIndividual(ICollection<ModelParameterGene<T>>)

GeneticBase<T, TInput, TOutput>.IndividualToModel(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>)

GeneticBase<T, TInput, TOutput>.GetEvolutionStats(IFitnessCalculator<T, TInput, TOutput>)

GeneticBase<T, TInput, TOutput>.ConfigureGeneticParameters(GeneticParameters)

GeneticBase<T, TInput, TOutput>.GetGeneticParameters()

GeneticBase<T, TInput, TOutput>.AddCrossoverOperator(string, Func<ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, double, ICollection<ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>>>)

GeneticBase<T, TInput, TOutput>.AddMutationOperator(string, Func<ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>, double, ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>>)

GeneticBase<T, TInput, TOutput>.SavePopulation(string)

GeneticBase<T, TInput, TOutput>.SerializePopulation()

GeneticBase<T, TInput, TOutput>.LoadPopulation(string)

GeneticBase<T, TInput, TOutput>.DeserializePopulation(byte[])

GeneticBase<T, TInput, TOutput>.Predict(TInput)

GeneticBase<T, TInput, TOutput>.GetMetaData()

GeneticBase<T, TInput, TOutput>.Serialize()

GeneticBase<T, TInput, TOutput>.SerializeIndividual(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>)

GeneticBase<T, TInput, TOutput>.SerializeModelData()

GeneticBase<T, TInput, TOutput>.Deserialize(byte[])

GeneticBase<T, TInput, TOutput>.DeserializeIndividual(byte[])

GeneticBase<T, TInput, TOutput>.DeserializeModelData(byte[])

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Constructors

NSGAII(Func<IFullModel<T, TInput, TOutput>>, List<IFitnessCalculator<T, TInput, TOutput>>, IModelEvaluator<T, TInput, TOutput>)

public NSGAII(Func<IFullModel<T, TInput, TOutput>> modelFactory, List<IFitnessCalculator<T, TInput, TOutput>> objectives, IModelEvaluator<T, TInput, TOutput> modelEvaluator)

Parameters

modelFactory Func<IFullModel<T, TInput, TOutput>>

objectives List<IFitnessCalculator<T, TInput, TOutput>>

modelEvaluator IModelEvaluator<T, TInput, TOutput>

Methods

CreateNextGeneration(TInput, TOutput, TInput?, TOutput?)

Creates the next generation of individuals through selection, crossover, and mutation.

protected override ICollection<ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>> CreateNextGeneration(TInput trainingInput, TOutput trainingOutput, TInput? validationInput = default, TOutput? validationOutput = default)

Parameters

trainingInput TInput

The input training data.

trainingOutput TOutput

The expected output for training.

validationInput TInput

Optional validation input data.

validationOutput TOutput

Optional validation output data.

Returns

ICollection<ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>>

The new population.

GetMetaData()

Gets the metadata for the model.

public override ModelMetadata<T> GetMetaData()

Returns

ModelMetadata<T>

The model metadata.