Uses of Interface
ec.Setup

Packages that use Setup

com.parabon.ec
com.parabon.ec.simple
ec
ec.app.ant
ec.app.ant.func
ec.app.coevolve1
ec.app.coevolve2
ec.app.ecsuite
ec.app.edge
ec.app.edge.func
ec.app.lawnmower
ec.app.lawnmower.func
ec.app.multiplexer	The Koza-I Boolean-Multiplexer problem.
ec.app.multiplexer.func
ec.app.parity
ec.app.parity.func
ec.app.regression
ec.app.regression.func
ec.app.sum
ec.app.twobox
ec.app.twobox.func
ec.breed
ec.coevolve
ec.de	Differential Evolution Algorithms.
ec.es
ec.eval
ec.exchange
ec.gp
ec.gp.breed
ec.gp.build
ec.gp.koza
ec.multiobjective
ec.multiobjective.spea2	Strength Pareto Evolutionary Algorithm implementation.
ec.parsimony
ec.pso
ec.rule
ec.rule.breed
ec.select
ec.simple
ec.spatial
ec.steadystate
ec.vector
ec.vector.breed

Uses of Setup in com.parabon.ec

Classes in com.parabon.ec that implement Setup

class

DummyStatistics This is a dummy object whose methods do nothing.

Uses of Setup in com.parabon.ec.simple

Classes in com.parabon.ec.simple that implement Setup

class	FinalStatistics Like SimpleStatistics, but also prints out all individuals in the population.
class	SimpleBoundedFitness A SimpleBoundedFitness is a SimpleFitness with an arbitrary upper bound.

Uses of Setup in ec

Subinterfaces of Setup in ec

interface	Clique Clique is a class pattern marking classes which create only a few instances, generally accessible through some global mechanism, and every single one of which gets its own distinct setup(...) call.
interface	Group Groups are used for populations and subpopulations.
interface	Prototype Prototype classes typically have one or a few prototype instances created during the course of a run.
interface	Singleton A Singleton is a class for which there will be only one instance in the entire course of a run, and which will exist for pretty much the entire run.

Classes in ec that implement Setup

class	Breeder A Breeder is a singleton object which is responsible for the breeding process during the course of an evolutionary run.
class	BreedingPipeline A BreedingPipeline is a BreedingSource which provides "fresh" individuals which can be used to fill a new population.
class	BreedingSource A BreedingSource is a Prototype which provides Individuals to populate new populations based on old ones.
class	Evaluator An Evaluator is a singleton object which is responsible for the evaluation process during the course of an evolutionary run.
class	EvolutionState An EvolutionState object is a singleton object which holds the entire state of an evolutionary run.
class	Exchanger The Exchanger is a singleton object whose job is to (optionally) perform individual exchanges between subpopulations in the run, or exchange individuals with other concurrent evolutionary run processes, using sockets or whatever.
class	Finisher Finisher is a singleton object which is responsible for cleaning up a population after a run has completed.
class	Fitness Fitness is a prototype which describes the fitness of an individual.
class	Individual An Individual is an item in the EC population stew which is evaluated and assigned a fitness which determines its likelihood of selection.
class	Initializer The Initializer is a singleton object whose job is to initialize the population at the beginning of the run.
class	Population A Population is the repository for all the Individuals being bred or evaluated in the evolutionary run at a given time.
class	Problem Problem is a prototype which defines the problem against which we will evaluate individuals in a population.
class	SelectionMethod A SelectionMethod is a BreedingSource which provides direct IMMUTABLE pointers to original individuals in an old population, not fresh mutable copies.
class	Species Species is a prototype which defines the features for a set of individuals in the population.
class	Statistics Statistics and its subclasses are Cliques which generate statistics during the run.
class	Subpopulation Subpopulation is a group which is basically an array of Individuals.

Uses of Setup in ec.app.ant

Classes in ec.app.ant that implement Setup

class	Ant Ant implements the Artificial Ant problem.
class	AntData Since Ant doesn't actually pass any information, this object is effectively empty.
class	AntStatistics

Uses of Setup in ec.app.ant.func

Classes in ec.app.ant.func that implement Setup

class	IfFoodAhead
class	Left
class	Move
class	Progn2
class	Progn3
class	Progn4
class	Right

Uses of Setup in ec.app.coevolve1

Classes in ec.app.coevolve1 that implement Setup

class

CompetitiveMaxOne

Uses of Setup in ec.app.coevolve2

Classes in ec.app.coevolve2 that implement Setup

class

CoevolutionaryRosenbrock

Uses of Setup in ec.app.ecsuite

Classes in ec.app.ecsuite that implement Setup

class

ECSuite Several standard Evolutionary Computation functions are implemented: Rastrigin, De Jong's test suite F1-F4 problems (Sphere, Rosenbrock, Step, Noisy-Quartic), Booth (from [Schwefel, 1995]), and Griewangk.

Uses of Setup in ec.app.edge

Classes in ec.app.edge that implement Setup

class	Edge Edge implements the Symbolic Edge problem.
class	EdgeData
class	EdgeShortStatistics
class	EdgeStatistics

Uses of Setup in ec.app.edge.func

Classes in ec.app.edge.func that implement Setup

class	Accept
class	BAccept
class	BBud
class	BLoop
class	BStart
class	Bud
class	Double
class	Epsilon
class	Loop
class	One
class	Reverse
class	Split
class	Start
class	Zero

Uses of Setup in ec.app.lawnmower

Classes in ec.app.lawnmower that implement Setup

class	Lawnmower Lawnmower implements the Koza-II Lawnmower problem.
class	LawnmowerData
class	LawnmowerStatistics

Uses of Setup in ec.app.lawnmower.func

Classes in ec.app.lawnmower.func that implement Setup

class	Frog
class	LawnERC
class	Mow
class	V8a

Uses of Setup in ec.app.multiplexer

Classes in ec.app.multiplexer that implement Setup

class	Multiplexer Multiplexer implements the family of n-Multiplexer problems.
class	MultiplexerData This is ugly and complicated because it needs to hold a variety of different-length bitstrings, including temporary ones held while computing subtrees.

Uses of Setup in ec.app.multiplexer.func

Classes in ec.app.multiplexer.func that implement Setup

class	A0
class	A1
class	A2
class	And
class	D0
class	D1
class	D2
class	D3
class	D4
class	D5
class	D6
class	D7
class	If
class	Not
class	Or

Uses of Setup in ec.app.parity

Classes in ec.app.parity that implement Setup

class	Parity Parity implements the family of n-[even|odd]-Parity problems up to 32-parity.
class	ParityData

Uses of Setup in ec.app.parity.func

Classes in ec.app.parity.func that implement Setup

class	D10
class	D11
class	D12
class	D13
class	D14
class	D15
class	D16
class	D17
class	D18
class	D19
class	D20
class	D21
class	D22
class	D23
class	D24
class	D25
class	D26
class	D27
class	D28
class	D29
class	D30
class	D31
class	D8
class	D9
class	Nand
class	Nor

Uses of Setup in ec.app.regression

Classes in ec.app.regression that implement Setup

class	Quintic Quintic implements a Symbolic Regression problem.
class	Regression Regression implements the Koza (quartic) Symbolic Regression problem.
class	RegressionData
class	Sextic Sextic implements a Symbolic Regression problem.

Uses of Setup in ec.app.regression.func

Classes in ec.app.regression.func that implement Setup

class	Add
class	Cos
class	Div
class	Exp
class	Log
class	Mul
class	RegERC
class	Sin
class	Sub
class	X

Uses of Setup in ec.app.sum

Classes in ec.app.sum that implement Setup

class

Sum Sum is a simple example of the ec.Vector package, implementing the very simple sum problem (fitness = sum over vector).

Uses of Setup in ec.app.twobox

Classes in ec.app.twobox that implement Setup

class	TwoBox TwoBox implements the TwoBox problem, with or without ADFs, as discussed in Koza-II.
class	TwoBoxData

Uses of Setup in ec.app.twobox.func

Classes in ec.app.twobox.func that implement Setup

class	H0
class	H1
class	L0
class	L1
class	W0
class	W1

Uses of Setup in ec.breed

Classes in ec.breed that implement Setup

class	BufferedBreedingPipeline If empty, a BufferedBreedingPipeline makes a request of exactly num-inds individuals from a single child source; it then uses these individuals to fill requests (returning min each time), until the buffer is emptied, at which time it grabs exactly num-inds more individuals, and so on.
class	ForceBreedingPipeline ForceBreedingPipeline has one source.
class	GenerationSwitchPipeline GenerationSwitchPipeline is a simple BreedingPipeline which switches its source depending on the generation.
class	MultiBreedingPipeline MultiBreedingPipeline is a BreedingPipeline stores some n child sources; each time it must produce an individual or two, it picks one of these sources at random and has it do the production.
class	ReproductionPipeline ReproductionPipeline is a BreedingPipeline which simply makes a copy of the individuals it recieves from its source.

Uses of Setup in ec.coevolve

Classes in ec.coevolve that implement Setup

class	CompetitiveEvaluator CompetitiveEvaluator.java
class	MultiPopCoevolutionaryEvaluator MultiPopCoevolutionaryEvaluator.java

Uses of Setup in ec.de

Classes in ec.de that implement Setup

class	Best1BinDEBreeder Best1BinDEBreeder implements the DE/best/1/bin Differential Evolution algorithm.
class	DEBreeder DEBreeder provides a straightforward Differential Evolution (DE) breeder for the ECJ system.
class	DEStatistics DEStatistics provides a straightforward solution to one problem many existing ECJ statistics classes have when used in conjunction with Differential Evolution (DE), namely reporting the fitness of individuals after they have been evaluated.
class	Rand1EitherOrDEBreeder Rand1EitherOrDEBreeder implements the DE/rand/1/either-or Differential Evolution Algorithm, explored recently in the "Differential Evolution: A Practical Approach to Global Optimization" book by Kenneth Price, Rainer Storn, and Jouni Lampinen.
class	Rand1ExpDEBreeder Rand1ExpDEBreeder implements the DE/rand/1/exp Differential Evolution Algorithm, explored recently in the "Differential Evolution: A Practical Approach to Global Optimization" book by Kenneth Price, Rainer Storn, and Jouni Lampinen.

Uses of Setup in ec.es

Classes in ec.es that implement Setup

class	ESSelection ESSelection is a special SelectionMethod designed to be used with evolutionary strategies-type breeders.
class	MuCommaLambdaBreeder MuCommaLambdaBreeder is a Breeder which, together with ESSelection, implements the (mu,lambda) breeding strategy and gathers the comparison data you can use to implement a 1/5-rule mutation mechanism.
class	MuPlusLambdaBreeder MuPlusLambdaBreeder is a subclass of MuCommaLambdaBreeder which, together with ESSelection, implements the (mu + lambda) breeding strategy and gathers the comparison data you can use to implement a 1/5-rule mutation mechanism.

Uses of Setup in ec.eval

Classes in ec.eval that implement Setup

class

MasterProblem MasterProblem.java

Uses of Setup in ec.exchange

Classes in ec.exchange that implement Setup

class	InterPopulationExchange InterPopulationExchange is an Exchanger which implements a simple exchanger between subpopulations.
class	IslandExchange IslandExchange is an Exchanger which implements a simple but quite functional asynchronous island model for doing massive parallel distribution of evolution across beowulf clusters.

Uses of Setup in ec.gp

Subinterfaces of Setup in ec.gp

interface

GPNodeSelector GPNodeSelector is a Prototype which describes algorithms which select random nodes out of trees, typically marking them for mutation, crossover, or whatnot.

Classes in ec.gp that implement Setup

class	ADF An ADF is a GPNode which implements an "Automatically Defined Function", as described in Koza II.
class	ADFArgument An ADFArgument is a GPNode which represents an ADF's argument terminal, its counterpart which returns argument values in its associated function tree.
class	ADFContext ADFContext is the object pushed onto an ADF stack which represents the current context of an ADM or ADF function call, that is, how to get the argument values that argument_terminals need to return.
class	ADFStack ADFStack is a special data object used to hold ADF data.
class	ADM An ADM is an ADF which doesn't evaluate its arguments beforehand, but instead only evaluates them (and possibly repeatedly) when necessary at runtime.
class	ERC ERC is an abstract GPNode which implements Ephemeral Random Constants, as described in Koza I.
class	GPAtomicType A GPAtomicType is a simple, atomic GPType.
class	GPBreedingPipeline A GPBreedingPipeline is a BreedingPipeline which produces only members of some subclass of GPSpecies.
class	GPData GPData is the parent class of data transferred between GPNodes.
class	GPFunctionSet GPFunctionSet is a Clique which represents a set of GPNode prototypes forming a standard function set for forming certain trees in individuals.
class	GPIndividual GPIndividual is an Individual used for GP evolution runs.
class	GPInitializer GPInitializer is a SimpleInitializer which sets up all the Cliques, ( the initial [tree/node]constraints, types, and function sets) for the GP system.
class	GPNode GPNode is a GPNodeParent which is the abstract superclass of all GP function nodes in trees.
class	GPNodeBuilder GPNodeBuilder is a Prototype which defines the superclass for objects which create ("grow") GP trees, whether for population initialization, subtree mutation, or whatnot.
class	GPNodeConstraints A GPNodeConstraints is a Clique which defines constraint information common to many different GPNode functions, namely return types, child types, and number of children.
class	GPProblem A GPProblem is a Problem which is meant to efficiently handle GP evaluation.
class	GPSetType A GPSetType is a GPType which contains GPAtomicTypes in a set, and is used as a generic GP type.
class	GPSpecies GPSpecies is a simple individual which is suitable as a species for GP subpopulations.
class	GPTree GPTree is a GPNodeParent which holds the root GPNode of a tree of GPNodes.
class	GPTreeConstraints A GPTreeConstraints is a Clique which defines constraint information common to many different GPTree trees, namely the tree type, builder, and function set.
class	GPType GPType is a Clique which represents types in Strongly-Typed Genetic Programming (STGP).

Uses of Setup in ec.gp.breed

Classes in ec.gp.breed that implement Setup

class	InternalCrossoverPipeline InternalCrossoverPipeline picks two subtrees from somewhere within an individual, and crosses them over.
class	MutateAllNodesPipeline MutateAllNodesPipeline implements the AllNodes mutation algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98.
class	MutateDemotePipeline MutateDemotePipeline works very similarly to the DemoteNode algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98, and is also similar to the "insertion" operator found in Una-May O'Reilly's thesis, "An Analysis of Genetic Programming".
class	MutateERCPipeline MutateERCPipeline works very similarly to the "Gaussian" algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98.
class	MutateOneNodePipeline MutateOneNodesPipeline implements the OneNode mutation algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98.
class	MutatePromotePipeline MutatePromotePipeline works very similarly to the PromoteNode algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98, and is also similar to the "deletion" operator found in Una-May O'Reilly's thesis, "An Analysis of Genetic Programming".
class	MutateSwapPipeline MutateSwapPipeline works very similarly to the Swap algorithm described in Kumar Chellapilla, "A Preliminary Investigation into Evolving Modular Programs without Subtree Crossover", GP98.
class	RehangPipeline RehangPipeline picks a nonterminal node other than the root and "rehangs" it as a new root.

Uses of Setup in ec.gp.build

Classes in ec.gp.build that implement Setup

class	PTC1 PTC1 implements the "Strongly-typed Probabilistic Tree Creation 1 (PTC1)" algorithm described in
class	PTC2 PTC2 implements the "Strongly-typed Probabilistic Tree Creation 2 (PTC2)" algorithm described in
class	PTCFunctionSet PTCFunctionSet is a GPFunctionSet which adheres to PTCFunctionSetForm, and thus can be used with the PTC1 and PTC2 methods.
class	RandomBranch RandomBranch implements the Random_Branch tree generation method described in
class	RandTree
class	Uniform Uniform implements the algorithm described in

Uses of Setup in ec.gp.koza

Classes in ec.gp.koza that implement Setup

class	CrossoverPipeline CrossoverPipeline is a GPBreedingPipeline which performs a strongly-typed version of Koza-style "Subtree Crossover".
class	FullBuilder FullBuilder is a GPNodeBuilder which implements the FULL tree building method described in Koza I/II.
class	GrowBuilder GrowBuilder is a GPNodeBuilder which implements the GROW tree building method described in Koza I/II.
class	HalfBuilder HalfBuilder is a GPNodeBuilder which implements the RAMPED HALF-AND-HALF tree building method described in Koza I/II.
class	KozaBuilder
class	KozaFitness KozaFitness is a Fitness which stores an individual's fitness as described in Koza I.
class	KozaNodeSelector KozaNodeSelector is a GPNodeSelector which picks nodes in trees a-la Koza I, with the addition of having a probability of always picking the root.
class	KozaShortStatistics A Koza-style statistics generator, intended to be easily parseable with awk or other Unix tools.
class	KozaStatistics A simple Koza-style statistics generator.
class	MutationPipeline MutationPipeline is a GPBreedingPipeline which implements a strongly-typed version of the "Point Mutation" operator as described in Koza I.

Uses of Setup in ec.multiobjective

Classes in ec.multiobjective that implement Setup

class

MultiObjectiveFitness MultiObjectiveFitness is a subclass of Fitness which implements basic multi-objective mechanisms suitable for being used with a variety of multi-objective selection mechanisms, including ones using pareto-optimality.

Uses of Setup in ec.multiobjective.spea2

Classes in ec.multiobjective.spea2 that implement Setup

class	SPEA2Breeder Breeds each subpopulation separately, with no inter-population exchange, and using the SPEA2 approach.
class	SPEA2Evaluator The SPEA2Evaluator is a simple, non-coevolved generational evaluator which evaluates every single member of every subpopulation individually in its own problem space.
class	SPEA2MultiObjectiveFitness SPEA2MultiObjectiveFitness is a subclass of Fitness which implements basic multiobjective fitness functions along with support for the ECJ SPEA2 (Strength Pareto Evolutionary Algorithm) extensions.
class	SPEA2Subpopulation SPEA2Subpopulation is a simple subclass of Subpopulation which adds the archiveSize field.
class	SPEA2TournamentSelection Does a simple tournament selection, limited to the subpopulation it's working in at the time and only within the boundry of the SPEA2 archive (between 0-archiveSize).

Uses of Setup in ec.parsimony

Classes in ec.parsimony that implement Setup

class	BucketTournamentSelection Does a tournament selection, limited to the subpopulation it's working in at the time.
class	DoubleTournamentSelection
class	LexicographicTournamentSelection Does a simple tournament selection, limited to the subpopulation it's working in at the time.
class	ProportionalTournamentSelection This selection method adds parsimony pressure to the regular tournament selection.
class	RatioBucketTournamentSelection Does a tournament selection, limited to the subpopulation it's working in at the time.
class	TarpeianStatistics This Statistics subclass implements Poli's "Tarpeian" method of parsimony control, whereby some kill-proportion of above-average-sized individuals in each subpopulation have their fitnesses set to a very bad value, and marks them as already evaluated (so the Evaluator can skip them).

Uses of Setup in ec.pso

Classes in ec.pso that implement Setup

class	PSOBreeder PSOBreeder.java
class	PSOSubpopulation PSOSubpopulation.java

Uses of Setup in ec.rule

Classes in ec.rule that implement Setup

class	Rule Rule is an abstract class for describing rules.
class	RuleConstraints RuleConstraints is a class for constraints applicable to rules.
class	RuleIndividual RuleIndividual is an Individual with an array of RuleSets, each of which is a set of Rules.
class	RuleInitializer A SimpleInitializer subclass designed to be used with rules.
class	RuleSet RuleSet is a set of Rules, implemented straightforwardly as an arbitrary-length array of Rules.
class	RuleSetConstraints RuleSetConstraints is an basic class for constraints applicable to rulesets.
class	RuleSpecies RuleSpecies is a simple individual which is suitable as a species for rule sets subpopulations.

Uses of Setup in ec.rule.breed

Classes in ec.rule.breed that implement Setup

class	RuleCrossoverPipeline RuleCrossoverPipeline is a BreedingPipeline which implements a simple default crossover for RuleIndividuals.
class	RuleMutationPipeline RuleMutationPipeline is a BreedingPipeline which implements a simple default Mutation for RuleIndividuals.

Uses of Setup in ec.select

Classes in ec.select that implement Setup

class	BestSelection Picks among the best n individuals in a population in direct proportion to their absolute fitnesses as returned by their fitness() methods relative to the fitnesses of the other "best" individuals in that n.
class	FirstSelection Always picks the first individual in the subpopulation.
class	FitProportionateSelection Picks individuals in a population in direct proportion to their fitnesses as returned by their fitness() methods.
class	GreedyOverselection GreedyOverselection is a SelectionMethod which implements Koza-style fitness-proportionate greedy overselection.
class	MultiSelection MultiSelection is a SelectionMethod which stores some n subordinate SelectionMethods.
class	RandomSelection Picks a random individual in the subpopulation.
class	TournamentSelection Does a simple tournament selection, limited to the subpopulation it's working in at the time.

Uses of Setup in ec.simple

Classes in ec.simple that implement Setup

class	SimpleBreeder Breeds each subpopulation separately, with no inter-population exchange, and using a generational approach.
class	SimpleEvaluator The SimpleEvaluator is a simple, non-coevolved generational evaluator which evaluates every single member of every subpopulation individually in its own problem space.
class	SimpleEvolutionState A SimpleEvolutionState is an EvolutionState which implements a simple form of generational evolution.
class	SimpleExchanger A SimpleExchanger is a default Exchanger which, well, doesn't do anything.
class	SimpleFinisher SimpleFinisher is a default Finisher which doesn't do anything.
class	SimpleFitness A simple default fitness, consisting of a single floating-point value where fitness A is superior to fitness B if and only if A > B.
class	SimpleInitializer SimpleInitializer is a default Initializer which initializes a Population by calling the Population's populate(...) method.
class	SimpleShortStatistics A Simple-style statistics generator, intended to be easily parseable with awk or other Unix tools.
class	SimpleStatistics A basic Statistics class suitable for simple problem applications.

Uses of Setup in ec.spatial

Classes in ec.spatial that implement Setup

class	Spatial1DSubpopulation A Spatial1DSubpopulation is an EC subpopulation that is additionally embedded into a one-dimmensional space.
class	SpatialBreeder A slight modification of the simple breeder for spatially-embedded EAs.
class	SpatialMultiPopCoevolutionaryEvaluator SpatialMultiPopCoevolutionaryEvaluator implements a coevolutionary evaluator involving multiple spatially-embedded subpopulations.
class	SpatialTournamentSelection A slight modification of the tournament selection procedure for use with spatially-embedded EAs.

Uses of Setup in ec.steadystate

Classes in ec.steadystate that implement Setup

class	SteadyStateBreeder
class	SteadyStateEvaluator
class	SteadyStateEvolutionState

Uses of Setup in ec.vector

Classes in ec.vector that implement Setup

class	BitVectorIndividual BitVectorIndividual is a VectorIndividual whose genome is an array of booleans.
class	ByteVectorIndividual ByteVectorIndividual is a VectorIndividual whose genome is an array of bytes.
class	DoubleVectorIndividual DoubleVectorIndividual is a VectorIndividual whose genome is an array of doubles.
class	FloatVectorIndividual FloatVectorIndividual is a VectorIndividual whose genome is an array of floats.
class	FloatVectorSpecies FloatVectorSpecies is a subclass of VectorSpecies with special constraints for floating-point vectors, namely FloatVectorIndividual and DoubleVectorIndividual.
class	GeneVectorIndividual GeneVectorIndividual is a VectorIndividual whose genome is an array of VectorGenes.
class	GeneVectorSpecies GeneVectorSpecies is a subclass of VectorSpecies with special constraints for GeneVectorIndividuals.
class	IntegerVectorIndividual IntegerVectorIndividual is a VectorIndividual whose genome is an array of ints.
class	IntegerVectorSpecies IntegerVectorSpecies is a subclass of VectorSpecies with special constraints for integral vectors, namely ByteVectorIndividual, ShortVectorIndividual, IntegerVectorIndividual, and LongVectorIndividual.
class	LongVectorIndividual LongVectorIndividual is a VectorIndividual whose genome is an array of longs.
class	ShortVectorIndividual ShortVectorIndividual is a VectorIndividual whose genome is an array of shorts.
class	VectorGene VectorGene is an abstract superclass of objects which may be used in the genome array of GeneVectorIndividuals.
class	VectorIndividual VectorIndividual is the abstract superclass of simple individual representations which consist of vectors of values (booleans, integers, floating-point, etc.)
class	VectorSpecies VectorSpecies is a species which can create VectorIndividuals.

Uses of Setup in ec.vector.breed

Classes in ec.vector.breed that implement Setup

class	VectorCrossoverPipeline VectorCrossoverPipeline is a BreedingPipeline which implements a simple default crossover for VectorIndividuals.
class	VectorMutationPipeline VectorMutationPipeline is a BreedingPipeline which implements a simple default Mutation for VectorIndividuals.