All Classes and Interfaces
Class
Description
Evolutionary Algorithm Configuration.
Evolutionary Algorithm - Execution Context
Utility class providing common activation functions for NEAT (NeuroEvolution of Augmenting Topologies) neural
networks.
Mutation policy handler for NEAT (NeuroEvolution of Augmenting Topologies) add-connection mutations.
Mutation policy handler for NEAT (NeuroEvolution of Augmenting Topologies) add-node mutations.
TODO TEST THE SHIT OUT OF ME
A chromosome implementation that represents genetic information as a sequence of bits.
Represents the result of parent comparison during NEAT genetic crossover.
Base interface for all chromosome types in the genetic algorithm framework.
Factory interface for creating chromosome instances based on specifications.
Marker interface for chromosome specifications in evolutionary algorithms.
Container representing data stored in OpenCL device memory for GPU-accelerated evolutionary algorithm processing.
Represents a neural network connection in the NEAT (NeuroEvolution of Augmenting Topologies) algorithm.
Represents a pair of node indices defining a potential connection in NEAT neural networks.
Evolution Listener which writes the output of each generation to a CSV file
Functional interface for loading genotype data into OpenCL device memory for GPU-accelerated fitness evaluation.
A simple default evolution listener that outputs progress to System.out without requiring external logging
dependencies.
Delete N Last
Represents an OpenCL compute device with its capabilities and characteristics for GPU-accelerated evolutionary
algorithms.
Utility class providing predicate-based filters for selecting OpenCL devices in GPU-accelerated evolutionary
algorithms.
Utility class providing convenient methods for OpenCL device discovery and information queries.
A chromosome implementation that represents genetic information as an array of double-precision floating-point
values.
Double tournament selection strategy that combines fitness-based and parsimony-based selection to control bloat in
genetic programming and other evolutionary algorithms.
Evolutionary Algorithm Configuration.
Evolutionary Algorithm Configuration.
Evolutionary Algorithm - Execution Context
Defines multiple factory and helper methods to create and manage EAExecutionContexts
Main orchestrator class for evolutionary algorithms, managing the complete evolution process.
Factory class providing convenient methods for creating properly configured
EASystem instances.Specify an elitism based replacement strategy
Functional interface for monitoring and responding to evolution progress during genetic algorithm execution.
Evolution listener that logs the top N individuals from each generation.
Implements a feed-forward neural network for evaluating NEAT (NeuroEvolution of Augmenting Topologies) chromosomes.
Functional interface for evaluating the fitness of a genotype in an evolutionary algorithm.
Functional interface for asynchronous batch fitness evaluation in evolutionary algorithms.
Comparing parents based on their fitness
Facade interface for abstracting different fitness evaluation strategies in evolutionary algorithms.
Wrapper around
FitnessBulkAsync for computing the fitness of a populationWrapper around
Fitness for computing the fitness of a populationVirtual thread-based fitness evaluator that creates one virtual thread per individual evaluation.
Comparing parents based on fitness first and then their size in case of equal fitness.
Represents a multi-objective fitness vector for multi-objective optimization (MOO).
A chromosome implementation that represents genetic information as an array of single-precision floating-point
values.
Generational Replacement strategy
Represents a genotype in an evolutionary algorithm, which is a collection of chromosomes.
Pair of Genotype to its associated fitness
Utility class for generating initial populations of genotypes in evolutionary algorithms.
Defines multiple factory and helper methods to create and manage EAExecutionContexts appropriate for Genetic
Programming
GPU-specific evolutionary algorithm configuration that extends the core EA framework with OpenCL capabilities.
GPU-specific execution context that extends the core EA framework with OpenCL device selection capabilities.
Factory class for creating GPU-accelerated evolutionary algorithm systems using OpenCL.
GPU-accelerated fitness evaluator that leverages OpenCL for high-performance evolutionary algorithm execution.
Immutable implementation of
AddConnection.Builds instances of type
AddConnection.Immutable implementation of
AddNode.Builds instances of type
AddNode.Immutable implementation of
BitChromosomeSpec.Builds instances of type
BitChromosomeSpec.Immutable implementation of
ChromosomeFactoryProvider.Builds instances of type
ChromosomeFactoryProvider.Immutable implementation of
CLData.Builds instances of type
CLData.Immutable implementation of
CoefficientOperation.Builds instances of type
CoefficientOperation.Immutable implementation of
ColumnExtractor.Builds instances of type
ColumnExtractor.Immutable implementation of
Connection.Builds instances of type
Connection.Immutable implementation of
CreepMutation.Builds instances of type
CreepMutation.Immutable implementation of
CSVEvolutionListener.Builds instances of type
CSVEvolutionListener.Immutable implementation of
DeleteConnection.Builds instances of type
DeleteConnection.Immutable implementation of
DeleteNLast.Builds instances of type
DeleteNLast.Immutable implementation of
DeleteNode.Builds instances of type
DeleteNode.Immutable implementation of
Device.Builds instances of type
Device.Immutable implementation of
DoubleChromosomeSpec.Builds instances of type
DoubleChromosomeSpec.Immutable implementation of
DoubleTournament.Builds instances of type
DoubleTournament.Immutable implementation of
EAConfiguration.Builds instances of type
EAConfiguration.Immutable implementation of
EAConfigurationBulkAsync.Builds instances of type
EAConfigurationBulkAsync.Immutable implementation of
EAExecutionContext.Builds instances of type
EAExecutionContext.Immutable implementation of
Elitism.Builds instances of type
Elitism.Immutable implementation of
EvolutionResult.Builds instances of type
EvolutionResult.Immutable implementation of
EvolutionStep.Builds instances of type
EvolutionStep.Immutable implementation of
FitnessComparison.Builds instances of type
FitnessComparison.Immutable implementation of
FitnessSharing.Builds instances of type
FitnessSharing.Immutable implementation of
FitnessThenSizeComparison.Builds instances of type
FitnessThenSizeComparison.Immutable implementation of
FloatChromosomeSpec.Builds instances of type
FloatChromosomeSpec.Immutable implementation of
GenerationalReplacement.Builds instances of type
GenerationalReplacement.Immutable implementation of
GenotypeFitness.Builds instances of type
GenotypeFitness.Immutable implementation of
GPUEAConfiguration.Builds instances of type
GPUEAConfiguration.Immutable implementation of
GPUEAExecutionContext.Builds instances of type
GPUEAExecutionContext.Immutable implementation of
Individual.Builds instances of type
Individual.Immutable implementation of
InputOperation.Builds instances of type
InputOperation.Immutable implementation of
InputSpec.Builds instances of type
InputSpec.Immutable implementation of
IntChromosomeSpec.Builds instances of type
IntChromosomeSpec.Immutable implementation of
KernelExecutionContext.Builds instances of type
KernelExecutionContext.Immutable implementation of
KernelInfo.Builds instances of type
KernelInfo.Immutable implementation of
MultiCombinations.Builds instances of type
MultiCombinations.Immutable implementation of
MultiMutations.Builds instances of type
MultiMutations.Immutable implementation of
MultiPointArithmetic.Builds instances of type
MultiPointArithmetic.Immutable implementation of
MultiPointCrossover.Builds instances of type
MultiPointCrossover.Immutable implementation of
MultiSelections.Builds instances of type
MultiSelections.Immutable implementation of
MultiStageDescriptor.Builds instances of type
MultiStageDescriptor.Immutable implementation of
MultiTournaments.Builds instances of type
MultiTournaments.Immutable implementation of
NeatChromosomeSpec.Builds instances of type
NeatChromosomeSpec.Immutable implementation of
NeatCombination.Builds instances of type
NeatCombination.Immutable implementation of
NeatConnectionWeight.Builds instances of type
NeatConnectionWeight.Immutable implementation of
NeatSelection.Builds instances of type
NeatSelection.Immutable implementation of
NodeReplacement.Builds instances of type
NodeReplacement.Immutable implementation of
NormalDistribution.Builds instances of type
NormalDistribution.Immutable implementation of
NSGA2Selection.Builds instances of type
NSGA2Selection.Immutable implementation of
OpenCLExecutionContext.Builds instances of type
OpenCLExecutionContext.Immutable implementation of
Operation.Builds instances of type
Operation.Immutable implementation of
OrderCrossover.Builds instances of type
OrderCrossover.Immutable implementation of
PartialMutation.Builds instances of type
PartialMutation.Immutable implementation of
PickFirstParent.Builds instances of type
PickFirstParent.Immutable implementation of
Platform.Builds instances of type
Platform.Immutable implementation of
Program.Immutable implementation of
Program.Builds instances of type
Program.Builds instances of type
Program.Immutable implementation of
ProgramApplyRules.Builds instances of type
ProgramApplyRules.Immutable implementation of
ProgramRandomCombine.Builds instances of type
ProgramRandomCombine.Immutable implementation of
ProgramRandomMutate.Builds instances of type
ProgramRandomMutate.Immutable implementation of
ProgramRandomPrune.Builds instances of type
ProgramRandomPrune.Immutable implementation of
ProgramTreeChromosomeSpec.Builds instances of type
ProgramTreeChromosomeSpec.Immutable implementation of
ProportionalTournament.Builds instances of type
ProportionalTournament.Immutable implementation of
RandomMutation.Builds instances of type
RandomMutation.Immutable implementation of
RandomSelection.Builds instances of type
RandomSelection.Immutable implementation of
RouletteWheel.Builds instances of type
RouletteWheel.Immutable implementation of
Rule.Builds instances of type
Rule.Immutable implementation of
SelectAll.Builds instances of type
SelectAll.Immutable implementation of
SelectiveRefinementTournament.Builds instances of type
SelectiveRefinementTournament.Immutable implementation of
SingleKernelFitnessDescriptor.Builds instances of type
SingleKernelFitnessDescriptor.Immutable implementation of
SinglePointArithmetic.Builds instances of type
SinglePointArithmetic.Immutable implementation of
SinglePointCrossover.Builds instances of type
SinglePointCrossover.Immutable implementation of
SPEA2Replacement.Builds instances of type
SPEA2Replacement.Immutable implementation of
StageDescriptor.Builds instances of type
StageDescriptor.Immutable implementation of
SwapMutation.Builds instances of type
SwapMutation.Immutable implementation of
SwitchStateMutation.Builds instances of type
SwitchStateMutation.Immutable implementation of
TarpeianMethod.Builds instances of type
TarpeianMethod.Immutable implementation of
Tournament.Builds instances of type
Tournament.Immutable implementation of
TournamentNSGA2Selection.Builds instances of type
TournamentNSGA2Selection.Immutable implementation of
TrimTree.Builds instances of type
TrimTree.Immutable implementation of
UniformDistribution.Builds instances of type
UniformDistribution.Represents an individual in an evolutionary algorithm, consisting of a genotype and its associated fitness value.
Manages innovation numbers for the NEAT (NeuroEvolution of Augmenting Topologies) algorithm.
A chromosome implementation that represents genetic information as an array of integer values.
Specification for integer array chromosomes in evolutionary algorithms.
Represents kernel-specific execution characteristics and resource requirements for an OpenCL kernel on a specific
device.
Utility class providing convenient methods for querying OpenCL kernel work group information.
Select uniformly a mutation policy among a list
GPU-accelerated fitness evaluator that executes multiple sequential OpenCL kernels for complex fitness computation.
Marker interface for mutation policy specifications in evolutionary algorithms.
Functional interface for applying mutation operations to genotypes in evolutionary algorithms.
Represents a neural network chromosome in the NEAT (NeuroEvolution of Augmenting Topologies) algorithm.
Chromosome mutation handler that adds new connections to NEAT (NeuroEvolution of Augmenting Topologies) neural
networks.
Implements genetic crossover for NEAT (NeuroEvolution of Augmenting Topologies) neural network chromosomes.
Specification for NEAT (NeuroEvolution of Augmenting Topologies) neural network chromosomes.
Configuration policy for NEAT (NeuroEvolution of Augmenting Topologies) genetic crossover operations.
Chromosome combinator handler for NEAT (NeuroEvolution of Augmenting Topologies) genetic crossover.
Factory for creating fully-connected initial NEAT (NeuroEvolution of Augmenting Topologies) chromosomes.
Mutation policy handler for NEAT (NeuroEvolution of Augmenting Topologies) connection weight mutations.
Factory class for creating NEAT (NeuroEvolution of Augmenting Topologies) execution contexts.
Selection policy for NEAT (NeuroEvolution of Augmenting Topologies) species-based selection.
Selection policy handler for NEAT (NeuroEvolution of Augmenting Topologies) species-based selection.
Concrete implementation of species-based selection for NEAT (NeuroEvolution of Augmenting Topologies) algorithm.
Utility class providing core algorithmic operations for the NEAT (NeuroEvolution of Augmenting Topologies) algorithm.
NSGA2 Selection specification
Provide a method to compute distances between to fitness scores along one objective
Encapsulates a complete OpenCL execution environment for a specific device with compiled kernels and runtime context.
Abstract base class for implementing OpenCL-based fitness evaluation in GPU-accelerated evolutionary algorithms.
Represents an operation (function or terminal) in genetic programming.
Factory interface for creating operations in genetic programming.
Specify the goal, whether to minimize or maximize the fitness score
Interface for handling parent comparison strategies during NEAT genetic crossover.
Represents an OpenCL platform providing access to compute devices and their capabilities.
Utility class providing predicate-based filters for selecting OpenCL platforms in GPU-accelerated evolutionary
algorithms.
Utility class providing convenient methods for OpenCL platform discovery and information queries.
Represents a population of individuals in an evolutionary algorithm.
Iterator implementation for traversing individuals in a population during evolutionary algorithms.
Specification for OpenCL programs containing kernel source code, build options, and compilation settings.
Marker interface for replacement strategy specifications in evolutionary algorithms.
Utility class for extracting computation results from OpenCL device memory after GPU kernel execution.
Marker interface for selection policy specifications in evolutionary algorithms.
Selective Refinement Tournament selection strategy that enhances traditional tournament selection by applying an
additional refinement step to a subset of candidates.
Builder class for constructing SelectiveRefinementTournament instances.
Functional interface for selecting individuals from a population in evolutionary algorithms.
GPU-accelerated fitness evaluator that executes a single OpenCL kernel for fitness computation.
Describes all the necessary information to execute an OpenCL kernel
Represents a species in the NEAT (NeuroEvolution of Augmenting Topologies) algorithm.
Generates unique identifiers for NEAT (NeuroEvolution of Augmenting Topologies) species.
Fully describes how to execute a specific stage with OpenCL
Functional interface for determining when to stop the evolutionary algorithm.
Utility class providing factory methods for creating common termination conditions in evolutionary algorithms.
Tournament based NSGA2 selection
A chromosome implementation that represents genetic information as a tree structure.
Represents a node in a tree structure used for genetic programming and tree-based chromosomes.
Ensure no tree will have a greater depth than allowed