Class AdaptiveGeneticAlgorithm<T, TInput, TOutput>

Namespace

AiDotNet.Genetics

Assembly

AiDotNet.dll

public class AdaptiveGeneticAlgorithm<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>

AdaptiveGeneticAlgorithm<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

AdaptiveGeneticAlgorithm(Func<IFullModel<T, TInput, TOutput>>, IFitnessCalculator<T, TInput, TOutput>, IModelEvaluator<T, TInput, TOutput>, double, double, double, double)

public AdaptiveGeneticAlgorithm(Func<IFullModel<T, TInput, TOutput>> modelFactory, IFitnessCalculator<T, TInput, TOutput> fitnessCalculator, IModelEvaluator<T, TInput, TOutput> modelEvaluator, double minMutationRate = 0.001, double maxMutationRate = 0.5, double minCrossoverRate = 0.4, double maxCrossoverRate = 0.95)

Parameters

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

fitnessCalculator IFitnessCalculator<T, TInput, TOutput>

modelEvaluator IModelEvaluator<T, TInput, TOutput>

minMutationRate double

maxMutationRate double

minCrossoverRate double

maxCrossoverRate double

Methods

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

Evolves the population for a specified number of generations.

public override EvolutionStats<T, TInput, TOutput> Evolve(int generations, TInput trainingInput, TOutput trainingOutput, TInput? validationInput = default, TOutput? validationOutput = default, Func<EvolutionStats<T, TInput, TOutput>, bool>? stopCriteria = null)

Parameters

generations int

The number of generations to evolve.

trainingInput TInput

The input training data used for fitness evaluation.

trainingOutput TOutput

The expected output for training used for fitness evaluation.

validationInput TInput

Optional validation input data.

validationOutput TOutput

Optional validation output data.

stopCriteria Func<EvolutionStats<T, TInput, TOutput>, bool>

Optional function that determines when to stop evolution.

Returns

EvolutionStats<T, TInput, TOutput>

Statistics about the evolutionary process.

GetMetaData()

Gets the metadata for the model.

public override ModelMetadata<T> GetMetaData()

Returns

ModelMetadata<T>

The model metadata.

UpdateEvolutionStats()

Updates the evolution statistics based on the current population.

protected override void UpdateEvolutionStats()