| Modifier and Type | Field and Description |
|---|---|
Individual[] |
Fitness.context
Auxiliary variable, used by coevolutionary processes, to store the individuals
involved in producing this given Fitness value.
|
Individual |
Species.i_prototype
The prototypical individual for this species.
|
Individual[] |
Subpopulation.individuals
The subpopulation's individuals.
|
| Modifier and Type | Method and Description |
|---|---|
Individual[] |
Fitness.getContext()
Treat the Individual[] you receive from this as read-only.
|
Individual |
Species.newIndividual(EvolutionState state,
DataInput dataInput)
Provides an individual read from a DataInput source, including
the fitness.
|
Individual |
Species.newIndividual(EvolutionState state,
int thread)
Provides a brand-new individual to fill in a population.
|
Individual |
Species.newIndividual(EvolutionState state,
LineNumberReader reader)
Provides an individual read from a stream, including
the fitness; the individual will
appear as it was written by printIndividual(...).
|
protected Individual |
Exchanger.process(EvolutionState state,
int thread,
String island,
int subpop,
Individual ind)
Typically called by preBreedingExchangePopulation prior to migrating an individual.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Problem.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log)
Part of SimpleProblemForm.
|
void |
Problem.describe(Individual ind,
EvolutionState state,
int subpopulation,
int threadnum,
int log,
int verbosity)
Deprecated.
Use the version without verbosity
|
double |
Individual.distanceTo(Individual otherInd)
Returns the metric distance to another individual, if such a thing can be measured.
|
void |
Statistics.individualsBredStatistics(SteadyStateEvolutionState state,
Individual[] individuals)
STEADY-STATE: called each time new individuals are bred during the steady-state
process.
|
void |
Statistics.individualsEvaluatedStatistics(SteadyStateEvolutionState state,
Individual[] newIndividuals,
Individual[] oldIndividuals,
int[] subpopulations,
int[] indices)
STEADY-STATE: called each time new individuals are evaluated during the steady-state
process.
|
void |
Statistics.individualsEvaluatedStatistics(SteadyStateEvolutionState state,
Individual[] newIndividuals,
Individual[] oldIndividuals,
int[] subpopulations,
int[] indices)
STEADY-STATE: called each time new individuals are evaluated during the steady-state
process.
|
void |
Individual.merge(EvolutionState state,
Individual other)
Replaces myself with the other Individual, while merging our evaluation results together.
|
protected Individual |
Exchanger.process(EvolutionState state,
int thread,
String island,
int subpop,
Individual ind)
Typically called by preBreedingExchangePopulation prior to migrating an individual.
|
int |
SelectionMethod.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
abstract int |
BreedingSource.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Produces n individuals from the given subpopulation
and puts them into inds[start...start+n-1],
where n = Min(Max(q,min),max), where q is the "typical" number of
individuals the BreedingSource produces in one shot, and returns
n.
|
int |
BreedingPipeline.reproduce(int n,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread,
boolean produceChildrenFromSource)
Performs direct cloning of n individuals.
|
void |
Fitness.setContext(Individual[] cont) |
void |
Fitness.setContext(Individual[] cont,
int index) |
| Modifier and Type | Method and Description |
|---|---|
void |
Ant.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
Ant.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
BBOBenchmarks.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
CompetitiveMaxOnes.evaluate(EvolutionState state,
Individual[] ind,
boolean[] updateFitness,
boolean countVictoriesOnly,
int[] subpops,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
CoevolutionaryECSuite.evaluate(EvolutionState state,
Individual[] ind,
boolean[] updateFitness,
boolean countVictoriesOnly,
int[] subpops,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
ECSuite.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
Edge.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
String |
Edge.describeShortGeneralized(Individual ind,
EvolutionState state,
int subpopulation,
int threadnum) |
void |
Edge.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
void |
Edge.fullTest(EvolutionState state,
Individual ind,
int threadnum,
boolean[][] pos,
boolean[][] neg)
Tests an individual, returning its successful positives
in totpos and its successful negatives in totneg.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Semantic.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
Semantic.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
HIFF.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
KLandscapes.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
Lawnmower.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
Lawnmower.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
Lid.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
Lid.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
MajorityGA.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
MajorityGP.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
MajorityGA.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
void |
MajorityGP.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Class and Description |
|---|---|
class |
MonaVectorIndividual |
| Modifier and Type | Field and Description |
|---|---|
Individual |
MonaStatistics.best_of_run |
| Modifier and Type | Method and Description |
|---|---|
void |
Mona.describe(EvolutionState state,
Individual ind,
int threadnum,
int subpopulation,
int log) |
void |
Mona.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
MooSuite.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
Multiplexer.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
Multiplexer.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
NK.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
OrderTree.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
Parity.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
Regression.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
Benchmarks.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
Regression.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
void |
Benchmarks.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
RoyalTree.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
SAT.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum)
Evalutes the individual using the MAXSAT fitness function.
|
| Modifier and Type | Method and Description |
|---|---|
void |
Sum.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
MaxOnes.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
AddSubtract.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
int |
OurMutatorPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
| Modifier and Type | Method and Description |
|---|---|
void |
OddRosenbrock.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
MultiValuedRegression.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
void |
TwoBox.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Field and Description |
|---|---|
Individual[] |
BufferedBreedingPipeline.buffer |
| Modifier and Type | Method and Description |
|---|---|
boolean |
CheckingPipeline.allValid(Individual[] inds,
int numInds,
int subpopulation,
EvolutionState state,
int thread) |
int |
InitializationPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
BufferedBreedingPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
CheckingPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
ReproductionPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
GenerationSwitchPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
ForceBreedingPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
MultiBreedingPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
| Modifier and Type | Field and Description |
|---|---|
(package private) Individual[][] |
MultiPopCoevolutionaryEvaluator.eliteIndividuals |
Individual |
EncapsulatedIndividual.ind |
Individual[] |
CompetitiveEvaluatorThread.inds |
(package private) Individual[] |
MultiPopCoevolutionaryEvaluator.inds |
(package private) Individual[] |
EliteComparator.inds |
| Modifier and Type | Method and Description |
|---|---|
protected Individual |
MultiPopCoevolutionaryEvaluator.produceCurrent(int subpopulation,
EvolutionState state,
int thread)
Selects one individual from the given subpopulation.
|
protected Individual |
MultiPopCoevolutionaryEvaluator.producePrevious(int subpopulation,
EvolutionState state,
int thread)
Selects one individual from the previous subpopulation.
|
| Modifier and Type | Method and Description |
|---|---|
void |
CompetitiveEvaluator.evalNRandomOneWay(EvolutionState state,
int[] from,
int[] numinds,
Individual[] individuals,
int subpop,
GroupedProblemForm prob) |
void |
CompetitiveEvaluator.evalNRandomOneWayPopChunk(EvolutionState state,
int from,
int numinds,
int threadnum,
Individual[] individuals,
int subpop,
GroupedProblemForm prob) |
void |
CompetitiveEvaluator.evalNRandomTwoWay(EvolutionState state,
int[] from,
int[] numinds,
Individual[] individuals,
int subpop,
GroupedProblemForm prob) |
void |
CompetitiveEvaluator.evalNRandomTwoWayPopChunk(EvolutionState state,
int from,
int numinds,
int threadnum,
Individual[] individuals,
int subpop,
GroupedProblemForm prob) |
void |
CompetitiveEvaluator.evalRoundRobin(EvolutionState state,
int[] from,
int[] numinds,
Individual[] individuals,
int subpop,
GroupedProblemForm prob) |
void |
CompetitiveEvaluator.evalRoundRobinPopChunk(EvolutionState state,
int from,
int numinds,
int threadnum,
Individual[] individuals,
int subpop,
GroupedProblemForm prob)
A private helper function for evalutatePopulation which evaluates a chunk
of individuals in a subpopulation for a given thread.
|
void |
CompetitiveEvaluator.evalSingleElimination(EvolutionState state,
Individual[] individuals,
int subpop,
GroupedProblemForm prob) |
void |
GroupedProblemForm.evaluate(EvolutionState state,
Individual[] ind,
boolean[] updateFitness,
boolean countVictoriesOnly,
int[] subpops,
int threadnum)
Evaluates the individuals found in ind together.
|
void |
CompetitiveEvaluator.randomizeOrder(EvolutionState state,
Individual[] individuals) |
| Constructor and Description |
|---|
EliteComparator(Individual[] inds) |
EncapsulatedIndividual(Individual ind_,
int value_) |
| Modifier and Type | Method and Description |
|---|---|
void |
SimpleIndividualPortrayal.portrayIndividual(EvolutionState state,
Individual individual) |
abstract void |
IndividualPortrayal.portrayIndividual(EvolutionState state,
Individual individual) |
| Modifier and Type | Method and Description |
|---|---|
int |
ESSelection.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
| Modifier and Type | Field and Description |
|---|---|
Individual[] |
MetaProblem.bestUnderlyingIndividual
The best underlying individual array, one per subpopulation.
|
(package private) Individual[] |
Job.inds |
(package private) Individual[] |
Job.newinds |
| Modifier and Type | Method and Description |
|---|---|
void |
MetaProblem.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
MasterProblem.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
MasterProblem.evaluate(EvolutionState state,
Individual[] inds,
boolean[] updateFitness,
boolean countVictoriesOnly,
int[] subpops,
int threadnum) |
(package private) void |
MasterProblem.evaluate(EvolutionState state,
Individual[] inds,
int[] subpopulations,
int threadnum) |
void |
MetaProblem.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
void |
MasterProblem.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
void |
MetaProblem.modifyParameters(EvolutionState state,
ParameterDatabase database,
int run,
Individual metaIndividual)
Override this method to revise the provided parameter database to reflect the "parameters" specified in the
given meta-individual.
|
(package private) static void |
Slave.returnIndividualsToMaster(EvolutionState state,
Individual[] inds,
boolean[] updateFitness,
DataOutputStream dataOut,
boolean returnIndividuals,
int individualInQuestion) |
| Modifier and Type | Field and Description |
|---|---|
Individual[][] |
IslandExchangeMailbox.immigrants
storage for the incoming immigrants: 2 sizes: the subpopulation and the index of the immigrant
|
(package private) Individual[][] |
InterPopulationExchange.immigrants |
| Modifier and Type | Class and Description |
|---|---|
class |
GPIndividual
GPIndividual is an Individual used for GP evolution runs.
|
| Modifier and Type | Method and Description |
|---|---|
Individual |
GPSpecies.newIndividual(EvolutionState state,
DataInput dataInput) |
Individual |
GPSpecies.newIndividual(EvolutionState state,
int thread) |
Individual |
GPSpecies.newIndividual(EvolutionState state,
LineNumberReader reader) |
| Modifier and Type | Method and Description |
|---|---|
int |
MutatePromotePipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
MutateAllNodesPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
InternalCrossoverPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
MutateOneNodePipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
MutateDemotePipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
SizeFairCrossoverPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
RehangPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
MutateERCPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
MutateSwapPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
| 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.
|
| Modifier and Type | Method and Description |
|---|---|
Individual |
GESpecies.newIndividual(EvolutionState state,
int thread)
This is an ugly hack to simulate the "Sensible Initialization",
First we create a GPIndividual, then reverse-map it to GEIndividuals,
We do not need to call IntegerVectorSpecies.newIndividual() since it is overriden
by the GPSpecies.newIndividual();
Moreover, as in the case for non-identical representations (i,e, GP-GE island
models etc,), the grammar rules, tree constraints, ERC's etc, are supposed to be
identical across all islands, so we are using the same "gpspecies" inside this class.
|
| Modifier and Type | Method and Description |
|---|---|
void |
GEProblem.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log) |
void |
GEProblem.evaluate(EvolutionState state,
Individual[] ind,
boolean[] updateFitness,
boolean countVictoriesOnly,
int[] subpops,
int threadnum)
Default version assumes that every individual is a GEIndividual.
|
void |
GEProblem.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum) |
| Modifier and Type | Method and Description |
|---|---|
int |
GETruncationPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
| Modifier and Type | Method and Description |
|---|---|
int |
CrossoverPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
MutationPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
| Modifier and Type | Method and Description |
|---|---|
static int[] |
MultiObjectiveFitness.getRankings(Individual[] inds)
Returns the Pareto rank for each individual.
|
static ArrayList |
MultiObjectiveFitness.partitionIntoParetoFront(Individual[] inds,
ArrayList front,
ArrayList nonFront)
Divides an array of Individuals into the Pareto front and the "nonFront" (everyone else).
|
static ArrayList |
MultiObjectiveFitness.partitionIntoRanks(Individual[] inds)
Divides inds into pareto front ranks (each an ArrayList), and returns them, in order,
stored in an ArrayList.
|
| Modifier and Type | Method and Description |
|---|---|
Individual[] |
NSGA2Evaluator.buildArchive(EvolutionState state,
int subpop)
Build the auxiliary fitness data and reduce the subpopulation to just the archive, which is
returned.
|
| Modifier and Type | Method and Description |
|---|---|
void |
NSGA2Evaluator.assignSparsity(Individual[] front)
Computes and assigns the sparsity values of a given front.
|
| Modifier and Type | Method and Description |
|---|---|
void |
SPEA2Breeder.buildArchive(EvolutionState state,
Individual[] oldInds,
Individual[] newInds,
int archiveSize) |
void |
SPEA2Breeder.buildArchive(EvolutionState state,
Individual[] oldInds,
Individual[] newInds,
int archiveSize) |
double[][] |
SPEA2Evaluator.calculateDistances(EvolutionState state,
Individual[] inds)
Returns a matrix of sum squared distances from each individual to each other individual.
|
double[] |
SPEA2Breeder.calculateDistancesFromIndividual(Individual ind,
Individual[] inds) |
double[] |
SPEA2Breeder.calculateDistancesFromIndividual(Individual ind,
Individual[] inds) |
void |
SPEA2Evaluator.computeAuxiliaryData(EvolutionState state,
Individual[] inds)
Computes the strength of individuals, then the raw fitness (wimpiness) and kth-closest sparsity
measure.
|
| Modifier and Type | Method and Description |
|---|---|
boolean |
LexicographicTournamentSelection.betterThan(Individual first,
Individual second,
int subpopulation,
EvolutionState state,
int thread) |
boolean |
ProportionalTournamentSelection.betterThan(Individual first,
Individual second,
int subpopulation,
EvolutionState state,
int thread) |
void |
TarpeianStatistics.setMinimumFitness(EvolutionState state,
int subpopulation,
Individual ind)
Sets the fitness of an individual to the minimum fitness possible.
|
| 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 |
RuleIndividual
RuleIndividual is an Individual with an array of RuleSets, each of which
is a set of Rules.
|
| Modifier and Type | Method and Description |
|---|---|
Individual |
RuleSpecies.newIndividual(EvolutionState state,
int thread) |
| Modifier and Type | Method and Description |
|---|---|
int |
RuleCrossoverPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
RuleMutationPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
| Modifier and Type | Method and Description |
|---|---|
boolean |
TournamentSelection.betterThan(Individual first,
Individual second,
int subpopulation,
EvolutionState state,
int thread)
Returns true if *first* is a better (fitter, whatever) individual than *second*.
|
int |
MultiSelection.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
RandomSelection.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
FirstSelection.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
| Modifier and Type | Field and Description |
|---|---|
Individual[] |
SimpleStatistics.best_of_run
The best individual we've found so far
|
Individual[] |
SimpleShortStatistics.bestOfGeneration |
Individual[] |
SimpleShortStatistics.bestSoFar |
(package private) Individual[] |
SimpleBreeder.EliteComparator.inds |
| Modifier and Type | Method and Description |
|---|---|
Individual[] |
SimpleShortStatistics.getBestSoFar() |
Individual[] |
SimpleStatistics.getBestSoFar() |
| Modifier and Type | Method and Description |
|---|---|
void |
SimpleProblemForm.describe(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum,
int log)
"Reevaluates" an individual,
for the purpose of printing out
interesting facts about the individual in the context of the
Problem, and logs the results.
|
void |
SimpleProblemForm.evaluate(EvolutionState state,
Individual ind,
int subpopulation,
int threadnum)
Evaluates the individual in ind, if necessary (perhaps
not evaluating them if their evaluated flags are true),
and sets their fitness appropriately.
|
| Constructor and Description |
|---|
SimpleBreeder.EliteComparator(Individual[] inds) |
| Modifier and Type | Method and Description |
|---|---|
protected Individual |
SpatialMultiPopCoevolutionaryEvaluator.produce(SelectionMethod method,
int subpopulation,
int individual,
EvolutionState state,
int thread) |
| Modifier and Type | Field and Description |
|---|---|
Individual |
QueueIndividual.ind |
| Modifier and Type | Method and Description |
|---|---|
Individual |
SteadyStateBreeder.breedIndividual(EvolutionState state,
int subpop,
int thread) |
Individual |
SteadyStateEvaluator.getNextEvaluatedIndividual()
Returns an evaluated individual is in the queue and ready to come back to us.
|
| Modifier and Type | Method and Description |
|---|---|
void |
SteadyStateEvaluator.evaluateIndividual(EvolutionState state,
Individual ind,
int subpop)
Submits an individual to be evaluated by the Problem, and adds it and its subpopulation to the queue.
|
void |
SteadyStateStatisticsForm.individualsBredStatistics(SteadyStateEvolutionState state,
Individual[] individuals)
Called each time new individuals are bred during the steady-state
process.
|
void |
SteadyStateStatisticsForm.individualsEvaluatedStatistics(SteadyStateEvolutionState state,
Individual[] newIndividuals,
Individual[] oldIndividuals,
int[] subpopulations,
int[] indices)
Called each time new individuals are evaluated during the steady-state
process, NOT including the initial generation's individuals.
|
void |
SteadyStateStatisticsForm.individualsEvaluatedStatistics(SteadyStateEvolutionState state,
Individual[] newIndividuals,
Individual[] oldIndividuals,
int[] subpopulations,
int[] indices)
Called each time new individuals are evaluated during the steady-state
process, NOT including the initial generation's individuals.
|
| Constructor and Description |
|---|
QueueIndividual(Individual i,
int s) |
| Modifier and Type | Class and Description |
|---|---|
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 |
GeneVectorIndividual
GeneVectorIndividual is a VectorIndividual whose genome is an array of Genes.
|
class |
IntegerVectorIndividual
IntegerVectorIndividual is a VectorIndividual whose genome is an array of ints.
|
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.)
|
| Modifier and Type | Method and Description |
|---|---|
Individual |
VectorSpecies.newIndividual(EvolutionState state,
int thread) |
| Modifier and Type | Method and Description |
|---|---|
double |
BitVectorIndividual.distanceTo(Individual otherInd)
Implements distance as hamming distance.
|
double |
LongVectorIndividual.distanceTo(Individual otherInd) |
double |
ByteVectorIndividual.distanceTo(Individual otherInd) |
double |
IntegerVectorIndividual.distanceTo(Individual otherInd) |
double |
ShortVectorIndividual.distanceTo(Individual otherInd) |
double |
FloatVectorIndividual.distanceTo(Individual otherInd) |
double |
DoubleVectorIndividual.distanceTo(Individual otherInd) |
| Modifier and Type | Method and Description |
|---|---|
int |
MultipleVectorCrossoverPipeline.multipleBitVectorCrossover(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Crosses over the Bit Vector Individuals using a
uniform crossover method.
|
int |
MultipleVectorCrossoverPipeline.multipleByteVectorCrossover(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Crosses over the Byte Vector Individuals using a
uniform crossover method.
|
int |
MultipleVectorCrossoverPipeline.multipleDoubleVectorCrossover(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Crosses over the Double Vector Individuals using a
uniform crossover method.
|
int |
MultipleVectorCrossoverPipeline.multipleFloatVectorCrossover(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Crosses over the Float Vector Individuals using a
uniform crossover method.
|
int |
MultipleVectorCrossoverPipeline.multipleGeneVectorCrossover(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Crosses over the Gene Vector Individuals using a
uniform crossover method.
|
int |
MultipleVectorCrossoverPipeline.multipleIntegerVectorCrossover(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Crosses over the Integer Vector Individuals using a uniform crossover method.
|
int |
MultipleVectorCrossoverPipeline.multipleLongVectorCrossover(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Crosses over the Long Vector Individuals using a
uniform crossover method.
|
int |
MultipleVectorCrossoverPipeline.multipleShortVectorCrossover(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread)
Crosses over the Short Vector Individuals using a
uniform crossover method.
|
int |
ListCrossoverPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
MultipleVectorCrossoverPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
GeneDuplicationPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
VectorCrossoverPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
int |
VectorMutationPipeline.produce(int min,
int max,
int start,
int subpopulation,
Individual[] inds,
EvolutionState state,
int thread) |
Copyright © 2014 Evolutionary Computation Laboratory at George Mason University. All rights reserved.