Class StandardGeneticAlgorithm<T, TInput, TOutput>

Namespace

AiDotNet.Genetics

Assembly

AiDotNet.dll

public class StandardGeneticAlgorithm<T, TInput, TOutput> : GeneticBase<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>

Implements

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

Derived

AdaptiveGeneticAlgorithm<T, TInput, TOutput>

IslandModelGeneticAlgorithm<T, TInput, TOutput>

NSGAII<T, TInput, TOutput>

SteadyStateGeneticAlgorithm<T, TInput, TOutput>

Inherited Members

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

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

public StandardGeneticAlgorithm(Func<IFullModel<T, TInput, TOutput>> modelFactory, IFitnessCalculator<T, TInput, TOutput> fitnessCalculator, IModelEvaluator<T, TInput, TOutput> modelEvaluator)

Parameters

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

fitnessCalculator IFitnessCalculator<T, TInput, TOutput>

modelEvaluator IModelEvaluator<T, TInput, TOutput>

Methods

CreateIndividual(ICollection<ModelParameterGene<T>>)

Creates a new individual with the specified genes.

public override ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>> CreateIndividual(ICollection<ModelParameterGene<T>> genes)

Parameters

genes ICollection<ModelParameterGene<T>>

The genes to include in the individual.

Returns

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

A new individual with the specified genes.

DeserializeIndividual(byte[])

Deserializes an individual from a byte array.

protected override ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>> DeserializeIndividual(byte[] data)

Parameters

data byte[]

The byte array containing the serialized individual.

Returns

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

The deserialized individual.

DeserializeModelData(byte[])

Deserializes model-specific data.

protected override void DeserializeModelData(byte[] data)

Parameters

data byte[]

The byte array containing the serialized model data.

DeserializePopulation(byte[])

Deserializes a population from a byte array.

protected override ICollection<ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>> DeserializePopulation(byte[] data)

Parameters

data byte[]

The byte array containing the serialized population.

Returns

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

The deserialized population.

GetMetaData()

Gets the metadata for the model.

public override ModelMetadata<T> GetMetaData()

Returns

ModelMetadata<T>

The model metadata.

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

Converts an individual to a trained model that can make predictions.

public override IFullModel<T, TInput, TOutput> IndividualToModel(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>> individual)

Parameters

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

The individual to convert.

Returns

IFullModel<T, TInput, TOutput>

A model capable of making predictions based on the individual's genes.

InitializePopulation(int, InitializationMethod)

Initializes a new population with random individuals.

public override ICollection<ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>>> InitializePopulation(int populationSize, InitializationMethod initializationMethod)

Parameters

populationSize int

The size of the population to create.

initializationMethod InitializationMethod

The method to use for initialization.

Returns

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

The newly created population.

MutateGene(ModelParameterGene<T>)

Creates a mutated version of a gene.

protected override ModelParameterGene<T> MutateGene(ModelParameterGene<T> gene)

Parameters

gene ModelParameterGene<T>

The gene to mutate.

Returns

ModelParameterGene<T>

A mutated copy of the gene.

MutateGeneGaussian(ModelParameterGene<T>)

Creates a mutated version of a gene using Gaussian noise.

protected override ModelParameterGene<T> MutateGeneGaussian(ModelParameterGene<T> gene)

Parameters

gene ModelParameterGene<T>

The gene to mutate.

Returns

ModelParameterGene<T>

A mutated copy of the gene.

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

Serializes an individual to a byte array.

protected override byte[] SerializeIndividual(ModelIndividual<T, TInput, TOutput, ModelParameterGene<T>> individual)

Parameters

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

The individual to serialize.

Returns

byte[]

A byte array containing the serialized individual.

SerializeModelData()

Serializes model-specific data.

protected override byte[] SerializeModelData()

Returns

byte[]

A byte array containing the serialized model data.

SerializePopulation()

Serializes the population to a byte array.

protected override byte[] SerializePopulation()

Returns

byte[]

A byte array containing the serialized population.