| Modifier and Type | Class and Description |
|---|---|
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 |
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 |
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Ant
Ant implements the Artificial Ant problem.
|
| Modifier and Type | Class and Description |
|---|---|
class |
IfFoodAhead |
class |
Move |
class |
Progn3 |
class |
Progn4 |
class |
Right |
| Modifier and Type | Class and Description |
|---|---|
class |
BBOBenchmarks
The Black Box Optimization workshop (BBOB) has an annual competition for doing real-valued parameter optimization.
|
| Modifier and Type | Class and Description |
|---|---|
class |
CompetitiveMaxOnes |
| Modifier and Type | Class and Description |
|---|---|
class |
CoevolutionaryECSuite |
| Modifier and Type | Class and Description |
|---|---|
class |
ECSuite
Several standard Evolutionary Computation functions are implemented.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Edge
Edge implements the Symbolic Edge problem.
|
class |
EdgeData |
| Modifier and Type | Class and Description |
|---|---|
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 |
| Modifier and Type | Class and Description |
|---|---|
class |
Semantic
Implements Goldberg and O'Reilly's semantic Order and Majority
problems.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SemanticExtra |
class |
SemanticJ |
class |
SemanticN0 |
class |
SemanticN1 |
class |
SemanticN10 |
class |
SemanticN11 |
class |
SemanticN12 |
class |
SemanticN13 |
class |
SemanticN14 |
class |
SemanticN15 |
class |
SemanticN16 |
class |
SemanticN2 |
class |
SemanticN3 |
class |
SemanticN4 |
class |
SemanticN5 |
class |
SemanticN6 |
class |
SemanticN7 |
class |
SemanticN8 |
class |
SemanticN9 |
class |
SemanticNode |
class |
SemanticX0 |
class |
SemanticX1 |
class |
SemanticX10 |
class |
SemanticX11 |
class |
SemanticX12 |
class |
SemanticX13 |
class |
SemanticX14 |
class |
SemanticX15 |
class |
SemanticX16 |
class |
SemanticX2 |
class |
SemanticX3 |
class |
SemanticX4 |
class |
SemanticX5 |
class |
SemanticX6 |
class |
SemanticX7 |
class |
SemanticX8 |
class |
SemanticX9 |
| Modifier and Type | Class and Description |
|---|---|
class |
HIFF
HIFF implements the Hierarchical If-And-Only-If problem developed by Watson, Hornby and Pollack.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KLandscapes
KLandscapes implements the K-Landscapes problem of Vanneschi,
Castelli and Manzoni.
|
| Modifier and Type | Class and Description |
|---|---|
class |
KLandscapeTree |
class |
KLandscapeTreeA |
class |
KLandscapeTreeB |
class |
KLandscapeTreeW |
class |
KLandscapeTreeX |
class |
KLandscapeTreeY |
class |
KLandscapeTreeZ |
| Modifier and Type | Class and Description |
|---|---|
class |
Lawnmower
Lawnmower implements the Koza-II Lawnmower problem.
|
class |
LawnmowerData |
| Modifier and Type | Class and Description |
|---|---|
class |
Frog |
class |
LawnERC |
class |
Left |
class |
Mow |
class |
Progn2 |
class |
V8a |
| Modifier and Type | Class and Description |
|---|---|
class |
Lid
Lid implements Daida's Lid problem.
|
| Modifier and Type | Class and Description |
|---|---|
class |
LidJ |
class |
LidX |
| Modifier and Type | Class and Description |
|---|---|
class |
MajorityData |
class |
MajorityGA
MajorityGA.java
Implements a GA-style vector rule for the one-dimensional Majority-Ones cellular automaton problem.
|
class |
MajorityGP
MajorityGP.java
Implements a GP-style vector rule for the one-dimensional Majority-Ones cellular automaton problem.
|
| Modifier and Type | Class and Description |
|---|---|
class |
E |
class |
EE |
class |
EEE |
class |
Nand |
class |
Nor |
class |
W |
class |
WW |
class |
WWW |
class |
X |
class |
Xor |
| Modifier and Type | Class and Description |
|---|---|
class |
Mona |
class |
MonaVectorIndividual |
| Modifier and Type | Class and Description |
|---|---|
class |
MooSuite
Several standard Multi-objective benchmarks are implemented:
ZDT1: Zitzler, Deb & Thiele
ZDT2: Zitzler, Deb & Thiele
ZDT3: Zitzler, Deb & Thiele
ZDT4: Zitzler, Deb & Thiele
ZDT6: Zitzler, Deb & Thiele
SPHERE: ftp.tik.ee.ethz.ch/pub/people/zitzler/ZLT2001a.pdf
SCH: (Schaffer), (a.k.a.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Multiplexer
Multiplexer implements the family of n-Multiplexer problems.
|
class |
MultiplexerData |
| Modifier and Type | Class and Description |
|---|---|
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 |
| Modifier and Type | Class and Description |
|---|---|
class |
NK
NK implmements the NK-landscape developed by Stuart Kauffman (in the book The Origins of
Order: Self-Organization and Selection in Evolution).
|
| Modifier and Type | Class and Description |
|---|---|
class |
OrderTree
OrderTree implements the OrderTree problem of Hoang et al.
|
| Modifier and Type | Class and Description |
|---|---|
class |
OrderTreeF0 |
class |
OrderTreeF1 |
class |
OrderTreeF2 |
class |
OrderTreeF3 |
class |
OrderTreeF4 |
class |
OrderTreeF5 |
class |
OrderTreeF6 |
class |
OrderTreeF7 |
class |
OrderTreeF8 |
class |
OrderTreeF9 |
class |
OrderTreeNode |
class |
OrderTreeT0 |
class |
OrderTreeT1 |
class |
OrderTreeT2 |
class |
OrderTreeT3 |
class |
OrderTreeT4 |
class |
OrderTreeT5 |
class |
OrderTreeT6 |
class |
OrderTreeT7 |
class |
OrderTreeT8 |
class |
OrderTreeT9 |
| Modifier and Type | Class and Description |
|---|---|
class |
Parity
Parity implements the family of n-[even|odd]-Parity problems up
to 32-parity.
|
class |
ParityData |
| Modifier and Type | Class and Description |
|---|---|
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 |
| Modifier and Type | Class and Description |
|---|---|
class |
Atan |
class |
Print |
class |
Regression |
| Modifier and Type | Class and Description |
|---|---|
class |
Benchmarks
Benchmarks by various people in the literature.
|
class |
Quintic
Quintic implements a Symbolic Regression problem.
|
class |
RegressionData |
class |
Sextic
Sextic implements a Symbolic Regression problem.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Cos |
class |
Cube |
class |
Exp |
class |
Inv |
class |
KeijzerERC |
class |
KornsERC |
class |
Log |
class |
Neg |
class |
NegExp |
class |
RegERC |
class |
Sin |
class |
Sqrt |
class |
Square |
class |
Tan |
class |
Tanh |
class |
VladERCA |
class |
VladERCB |
class |
VladERCC |
class |
X1 |
class |
X2 |
class |
X3 |
class |
X4 |
class |
X5 |
| Modifier and Type | Class and Description |
|---|---|
class |
RoyalTree
RoyalTree implements Punch's RoyalTree problem.
|
| Modifier and Type | Class and Description |
|---|---|
class |
RoyalTreeA |
class |
RoyalTreeB |
class |
RoyalTreeC |
class |
RoyalTreeD |
class |
RoyalTreeE |
class |
RoyalTreeNode |
class |
RoyalTreeX |
| Modifier and Type | Class and Description |
|---|---|
class |
SAT
SAT implements the boolean satisfiability problem.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Sum
Sum is a simple example of the ec.Vector package, implementing the
very simple sum problem (fitness = sum over vector).
|
| Modifier and Type | Class and Description |
|---|---|
class |
MaxOnes |
| Modifier and Type | Class and Description |
|---|---|
class |
AddSubtract |
class |
OurMutatorPipeline
OurMutatorPipeline is a BreedingPipeline which negates the sign of genes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
OddRosenbrock |
class |
OurSelection |
| Modifier and Type | Class and Description |
|---|---|
class |
DoubleData |
class |
MultiValuedRegression |
class |
Y |
| Modifier and Type | Class and Description |
|---|---|
class |
TwoBox
TwoBox implements the TwoBox problem, with or without ADFs,
as discussed in Koza-II.
|
class |
TwoBoxData |
| Modifier and Type | Class and Description |
|---|---|
class |
Add |
class |
Div |
class |
H0 |
class |
H1 |
class |
L0 |
class |
L1 |
class |
Mul |
class |
Sub |
class |
W0 |
class |
W1 |
| Modifier and Type | Class and Description |
|---|---|
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 |
CheckingPipeline
CheckingPipeline is a BreedingPipeline which just passes through the
individuals it receives from its source 0, but only if those individuals
ALL pass a validation check (the method allValid(), which you must override).
|
class |
ForceBreedingPipeline
ForceBreedingPipeline has one source.
|
class |
GenerationSwitchPipeline
GenerationSwitchPipeline is a simple BreedingPipeline which switches its source depending
on the generation.
|
class |
InitializationPipeline
InitializationPipeline is a BreedingPipeline which simply generates a new
random inidividual.
|
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
ESSelection
ESSelection is a special SelectionMethod designed to be used with
evolutionary strategies-type breeders.
|
| Modifier and Type | Class and Description |
|---|---|
class |
MasterProblem
MasterProblem.java
|
class |
MetaProblem
MetaProblem is a special class for implenting so-called "Meta-Evolutionary Algorithms",
a topic related to "HyperHeuristics".
|
| Modifier and Type | Interface and Description |
|---|---|
interface |
GPNodeSelector
GPNodeSelector is a Prototype which describes algorithms which
select random nodes out of trees, typically marking them for
mutation, crossover, or whatnot.
|
| Modifier and Type | Class and Description |
|---|---|
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 |
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 |
GPIndividual
GPIndividual is an Individual used for GP evolution runs.
|
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 |
GPProblem
A GPProblem is a Problem which is meant to efficiently handle GP
evaluation.
|
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.
|
| Modifier and Type | Class and Description |
|---|---|
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.
|
class |
SizeFairCrossoverPipeline
SizeFairCrossover works similarly to one written in the paper
"Size Fair and Homologous Tree Genetic Programming Crossovers" by Langdon (1998).
|
| Modifier and Type | Class and Description |
|---|---|
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 |
RandomBranch
RandomBranch implements the Random_Branch tree generation
method described in
|
class |
RandTree |
class |
Uniform
Uniform implements the algorithm described in
|
| Modifier and Type | Class and Description |
|---|---|
class |
GEIndividual
GEIndividual is a simple subclass of IntegerVectorIndividual which not only prints out (for humans)
the Individual as a int vector but also prints out the Individual's tree representation.
|
class |
GEProblem
GEProblem is a special replacement for Problem which performs GE mapping.
|
class |
GESpecies
GESpecies generates GPIndividuals from GEIndividuals through the application of a grammar parse graph
computed by the GrammarParser.
|
class |
GrammarParser
A GrammarParser is the basic class for parsing a GE ruleset into a parse graph of GrammarNodes.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GECrossoverPipeline
GECrossoverPipeline is just like ListCrossoverPipeline, except that it will additionally
check to verify that the first crossover point is within the range of consumed genes
in each parent.
|
class |
GETruncationPipeline
GETruncationPipeline removes the unused genes from the end of the vector.
|
| Modifier and Type | Class and Description |
|---|---|
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 |
MutationPipeline
MutationPipeline is a GPBreedingPipeline which
implements a strongly-typed version of the
"Point Mutation" operator as described in Koza I.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Nonterminal
ECJ implements Push's s-expressions as trees of nonterminals
and terminals.
|
class |
PushBuilder
PushBuilder implements the Push-style tree building algorithm, which permits nonterminals of arbitrary arity.
|
class |
PushInstruction
PushInstruction encapsulates a custom Push instruction.
|
class |
PushProblem
A PushProblem contains useful methods to help you create an
interpreter, write out the ECJ GP tree to a string, build a Push Program
around this string, load the interpreter with all your custom instructions,
and run the Push Program on the interpreter.
|
class |
Terminal
Terminal is the leaf node in Push trees and is used to represent Push
instructions of all types.
|
| Modifier and Type | Class and Description |
|---|---|
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
NSGA2MultiObjectiveFitness
NSGA2MultiObjectiveFitness is a subclass of MultiObjeciveFitness which
adds auxiliary fitness measures (sparsity, rank) largely used by MultiObjectiveStatistics.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SPEA2MultiObjectiveFitness
SPEA2MultiObjectiveFitness is a subclass of MultiObjectiveFitness which adds three auxiliary fitness
measures used in SPEA2: strength S(i), kthNNDistance D(i), and a final fitness value R(i) + D(i).
|
class |
SPEA2TournamentSelection
This is a special version of TournamentSelection which restricts the selection to only
the archive region (the top 'archiveSize' elements in the subpopulation).
|
| Modifier and Type | Class and Description |
|---|---|
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Particle
Particle is a DoubleVectorIndividual with additional statistical information
necessary to perform Particle Swarm Optimization.
|
| Modifier and Type | Class and Description |
|---|---|
class |
Rule
Rule is an abstract class for describing rules.
|
class |
RuleIndividual
RuleIndividual is an Individual with an array of RuleSets, each of which
is a set of Rules.
|
class |
RuleSet
RuleSet is a set of Rules, implemented straightforwardly as an arbitrary-length array of Rules.
|
class |
RuleSpecies
RuleSpecies is a simple individual which is suitable as a species
for rule sets subpopulations.
|
| Modifier and Type | Class and Description |
|---|---|
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BestSelection
Performs a tournament selection restricted to only the best, or worst, n
indivdiuals in the population.
|
class |
BoltzmannSelection
Similar to FitProportionateSelection, but with a Simulated Annealing style twist.
|
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 |
SigmaScalingSelection
Similar to FitProportionateSelection, but with adjustments to scale up/exaggerate differences in fitness for selection when true fitness values are very close to
eachother across the population.
|
class |
SUSSelection
Picks individuals in a population using the Stochastic Universal Selection (SUS) process, using
fitnesses as returned by their fitness() methods.
|
class |
TournamentSelection
Does a simple tournament selection, limited to the subpopulation it's
working in at the time.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SimpleFitness
A simple default fitness, consisting of a double floating-point value
where fitness A is superior to fitness B if and only if A > B.
|
| Modifier and Type | Class and Description |
|---|---|
class |
SpatialTournamentSelection
A slight modification of the tournament selection procedure for use with spatially-embedded EAs.
|
| Modifier and Type | Class and Description |
|---|---|
class |
BitVectorIndividual
BitVectorIndividual is a VectorIndividual whose genome is an array of booleans.
|
class |
BitVectorSpecies
BitVectorSpecies is a subclass of VectorSpecies with special
constraints for boolean vectors, namely BitVectorIndividual.
|
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 |
Gene
Gene is an abstract superclass of objects which may be used in
the genome array of GeneVectorIndividuals.
|
class |
GeneVectorIndividual
GeneVectorIndividual is a VectorIndividual whose genome is an array of Genes.
|
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 |
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.
|
| Modifier and Type | Class and Description |
|---|---|
class |
GeneDuplicationPipeline
GeneDuplicationPipeline is designed to duplicate a sequence of genes from the chromosome and append
them to the end of the chromosome.
|
class |
ListCrossoverPipeline
ListCrossoverPipeline is a crossover pipeline for vector individuals whose length
may be lengthened or shortened.
|
class |
MultipleVectorCrossoverPipeline
MultipleVectorCrossoverPipeline is a BreedingPipeline which implements a uniform
(any point) crossover between multiple vectors.
|
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.
|
Copyright © 2014 Evolutionary Computation Laboratory at George Mason University. All rights reserved.