Table of Contents

Namespace AiDotNet.Genetics

Classes

AdaptiveGeneticAlgorithm<T, TInput, TOutput>
BinaryGene

Represents a gene that holds a binary value (0 or 1).

BinaryIndividual

Represents an individual encoded with binary genes, suitable for classic GA problems.

GeneticBase<T, TInput, TOutput>

Provides a base implementation of IGeneticModel that handles common genetic algorithm operations.

IslandModelGeneticAlgorithm<T, TInput, TOutput>
ModelIndividual<T, TInput, TOutput, TGene>

Represents an individual that is also a full model, allowing direct evolution of models without conversion between individuals and models.

ModelParameterGene<T>

Represents a gene that corresponds to a parameter in a machine learning model.

MultiObjectiveRealIndividual

A real-valued individual supporting multi-objective optimization.

NSGAII<T, TInput, TOutput>
NodeGene

Represents a node in a genetic programming tree.

PermutationGene

Represents a gene in a permutation (the index of an element in a sequence).

PermutationIndividual

Represents an individual encoded as a permutation, suitable for problems like TSP.

RealGene

Represents a gene with a real (double) value.

RealValuedIndividual

Represents an individual encoded with real-valued genes, suitable for numerical optimization problems.

StandardGeneticAlgorithm<T, TInput, TOutput>
SteadyStateGeneticAlgorithm<T, TInput, TOutput>
TreeIndividual

Represents an individual in genetic programming with a tree structure.