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Manhattan Distance. It is the correct name.IterationStrategy class for all population based algorithms.TopologyVisitor into the Topology to perform the actions
defined within the TopologyVisitor.
TopologyVisitor into the Topology to perform the actions
defined within the TopologyVisitor.
Visitor and
perform the Visitor.visit(Object) method.
Visitor and
perform the Visitor.visit(Object) method.
visitor to all elements contained in this Vector.
element to the list at index index.
entity to either the modifiable and unmodifiable
lists, maintained within this holder.
candidateSolution if no solution within the archive dominates it.
element to the current Structure.
element to the current Structure.
element to the current Structure.
element to the current Structure.
element to the current Structure.
element to the current Structure.
element to the end of the current Vector.
index.
candidateSolutions and adds the
non-dominated solutions to the archive.
E, within structureto the
current Structure.
E, within structureto the
current Structure.
E, within structureto the
current Structure.
E, within structureto the
current Structure.
E, within structureto the
current Structure.
structure to the current
Vector.
DataSetBuilder.addDataSet(DataSet) because it works
completely different than a normal DataSetBuilder.
cost for the connection.
cost and weight for the connection.
candidateSolution into the archive.
Algorithm.AlgorithmEvent with a given source algorithm.
Algorithm must satisfy this interface.IterationStrategy
to perform the iteration.
SolutionWeighing that weighs a collection of
OptimisationSolutions based on how closely clustered these solutions are to
one another.list to the end of the current Vector.
IterationStrategy class that wraps another IterationStrategy
and is responsible for populating the Archive of Pareto optimal solutions after the execution
of the inner IterationStrategy class.Algorithm instances.
DataSets specified
through the AssociatedPairDataSetBuilder.addDataSet(DataSet) method.BitArray with the initial number of bits
equal to 32.
BitArray with the initial number of bits specified
by the parameter numberOfBits.
Blackboard container.
Entity instances that are
operating in the current search space.BoundedControlParameter.
Fitness of the current Problem instance
based on the provided solution.
Entity.
Fitness of the current Problem instance
based on the provided solution.
Entity incrementing the
number of fitness evaluations for the algorithm.
Entity.
Entity incrementing the
number of fitness evaluations for the algorithm.
Entity.
Entity incrementing the
number of fitness evaluations for the algorithm.
Entity.
Entity incrementing the
number of fitness evaluations for the algorithm.
Entity.
ClusteringProblem.innerProblem, so use it to calculate the
fitness.
Fitness of the current Problem instance
based on the provided solution.
Fitness of the current Problem instance
based on the provided solution.
Fitness of the current Problem instance
based on the provided solution.
Fitness of the current Problem instance
based on the provided solution.
Fitness of the current Problem instance
based on the provided solution.
Fitness of the current Problem instance
based on the provided solution.
Entity incrementing the
number of fitness evaluations for the algorithm.
Entity.
Entity.
ClusterCenterStrategy.
CandidateSoution.CandidateSolution is a potential solution within an optimization.CandidateSolutionMixin.
Minkowski Metric with 'alpha' := infinity.combination.
Entity from over-shooting the problem search space.Matrix of it's internal state.
Vector.
getClone() method that
can be used to obtain a cloned version of the instance on which the
method was invoked.Entity.ClonedPopulationInitialisationStrategy.
targetEntity
using a DistanceMeasure.ClusteringUtils
helper class.AssociatedPairDataSetBuilder.ClusteringProblem, a
ClusterableDataSet and a ClusteringFitnessFunction.AbstractShape collides in the current grid
n and r.
InferiorFitness instance.
Algorithm
instances.Operator instances.ConstantControlParameter.
Archive constrained by the number of solutions that it can store.true if this list contains the specified element.
element is contained within the Structure.
element is contained within the Structure.
element is contained within the Structure.
element is contained within the Structure.
element is contained within the Structure.
element is contained within the Structure.
element is contained within the Structure.
true if the specified element is contained
within the current Vector.
ContinuousFunction.
Bounds object, or alternatively return a precreated instance.
Bounds instance.
MOOptimisationProblem and converts it to a
single-objective optimisation problem by selecting one of the sub-objectives
as its active objective.Vector from
randomly chosen patterns in the dataset.Particle
using the DataSetBasedCentroidsInitialisationStrategy.DataSets and
DataSetBuilders that might be instantiated by a Simulation,
Problem or Thread.EnvironmentChangeDetectionStrategy.interval iterations, pick
a number of random entities from the given
algorithm's topology and compare their previous fitness values with
their current fitness values.
EnvironmentChangeDetectionStrategy.interval iterations, iterate through all sentry points
and compare their previous fitness values with their current fitness values.
visit method.StructuredType
instances.
StructuredType
instances.
Vector by the provided scalar.
Vector by the provided scalar.
GuideUpdateStrategy where a particle's guide
gets updated if and only if the new guide dominates the previous guide.candidateSolution.
reaction strategy wraps both a
ReinitializationReactionStrategy and a ReevaluationReactionStrategy.EC.
DefaultComparator.
Comparator.
Fitness of Entity instances.EntityFitness strategy.
Entry instances.
Set of key / value pairs.
Vector is equal to the provided object.
Vector is equal to the provided object.
obj is equal to the currently
decorated element within this Entry.
alpha = 2.ActivationFunction at the provided point.
ActivationFunction at the provided point.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
Vector as input.
net.sourceforge.cilib.type.types.Vector by
decoding the binary vector into a continuous vector and evaluate the results
by feeding the result into the wrapped funtion.
x.
x.
x.
NonlinearMappingProblem.
ExponentiallyDecreasingControlParameter.
ExponentiallyIncreasingControlParameter.
x.
FactoryException without detail message.
FactoryException with the specified detail message.
number of elements from the current selection.
Entity, decoupling the
Entity from the Problem.0.0.
Function.OptimisationProblemAdapter that can be used to find the minimum of
any Function.Function.FunctionOptimisationProblem with null function.
GBestTopology.
null key value
and zero subtrees.
element defined
as the key value and an empty set of subtrees.
index.
null otherwise.
Numeric at the provided index.
Entity within the Topology.
Entity within the current Topology, based
on the provided Comparator instance.
Topology.
Topology, based
on the provided Comparator.
x, given the values
for p and n.
Numeric at position index.
CandidateSoltion.
ClusterableDataSet used throughout the current clustering.
ClusteringProblem used throughout the current clustering.
VelocityUpdateStrategy.
Vector representing the column within the Matrix at the given index.
Matrix.
DataSetBuilder.
DataSetBuilder.
DataSetBuilder.
Vector that has been cached by the ClusteringUtils.clusterableDataSet.
ClusteringUtils.clusterableDataSet.
display() method to show
a GameItem
display() method to show
a GameItem
display() method to show
a GameItem
display() method to show
a GameItem
display() method to show
a GameItem
Vector.
ClusteringUtils.calculateDistance(Vector, Vector) which is the
central point for distance calculations during a clustering.
element that this Entry represents.
InitialisationStrategy.
CandidateSolution.
CandidateSolution.
OptimisationProblem.getFitness(Type, boolean).
position.
position.
position.
position.
Fitness of the provided entity.
Fitness of the provided entity.
Fitness of the provided entity.
FitnessCalculator for the current Entity.
FitnessCalculator for the current Entity.
mean of
0.0 and a deviation of 1.0.
mean and a deviation of
deviation.
ActivationFunction at the given point.
Vector for the provided input Vector.
ActivationFunction at the given point.
Vector for the provided input Vector.
Vector for the provided input Vector.
Vector for the provided input Vector.
Entity instance.
Entity instance.
id associated with this Topology, if
an id is defined.
id associated with this Topology, if
an id is defined.
ControlParameter representing the inerti weight of the VelocityUpdateStrategy.
PopulationInitialisationStrategy.
InputStream.
Numeric at position index.
EC.
IterationStrategy of the PSO algorithm.
M.
M.
Matrix.
Vector.
Topology) in a
Set (duplicates are not allowed) and return them.
NeighbourhoodBestUpdateStrategy.
NeighbourhoodBestUpdateStrategy.
id for the Entity.
getGaussian().
ClusteringUtils.clusterableDataSet.
List<Operator> that represents the sequence
of operators to be performed within the current IterationStrategy.
net.sourceforge.cilib.offspringList.operators.mutation.MutationOperatorStrategy.
min and max.
min and max.
min and max.
min and max.
min and max.
ClusteringUtils.clusterableDataSet.
Algorithm based on the
MinimumDiversity.calculatedDiversity, MinimumDiversity.maximumDiversity and MinimumDiversity.minimumDiversity.
Entity.
CandidateSolution.
CandidateSolution.
Entity.
Numeric at position index.
rho.
rho contraction.
rho expansion.
rho.
Vector representing the row within the Matrix at the given index.
Matrix.
measurement based on the current
provided algorithm.
0 <= x < 1.
VelocityUpdateStrategy
of the current particle.
index.
index.
ControlParameter representing the vMax component.
Entry.
Griewank.
PSOs the pBest and lBest (or gBest) particles are replaced with the
concept of local and global guides respectively.GuideSelectionStrategy to
determine when and if a particle's guides get updated.Vector.
element.
OptimisationProblem into sub-problems of unequal size/dimension.InitialisationException without detail message.
InitialisationException with the specified detail message.
Entity collection based on the given
Topology and Problem.
Entity collection based on the given
Topology and Problem.
DataSets should have been parsed and added to
AssociatedPairDataSetBuilder.patterns.
Particle from the
current dataset using the DataSetBasedCentroidsInitialisationStrategy.
Visitor to make it compatible
with In-Order Traversals in the Containers defined.index.
index.
Numeric at the specified index.
InferiorFitness instance.
lower
and upper.
StoppingCondition.getPercentageCompleted() == 1.0 but may be more efficient).
store it.
StoppingCondition.getPercentageCompleted() == 1.0 but may be more efficient).
StoppingCondition.getPercentageCompleted() == 1.0 but may be more efficient).
candidateSolution.
true if this list contains no elements.
true if the Vector contains no elements.
IterationBasedChangeStrategy is a test to ensure that a problem is
altered or changed at a predefined frequency.PopulationBasedAlgorithm.Vector iteratively.
MultiPopulationBasedAlgorithm
to enable different types of knowledge (like global best particle positions etc.) to
be shared among different sub-populations during a search.seed value.
number of elements from the current selection.
LBestTopology.
BitArray.
LFPSO.
LinearDecreasingControlParameter.
LinearDecreasingControlParameter.
[min, max].
value with the provided base.
lower
and upper.
Vector from vector Vector with each component's value
set to the lower bound of that component.
Minkowski Metric with 'alpha' := 1.java.lang.Math
class.Matrix object with dimensions: rows x columns.
MaximisationFitness with the given fitness value.
ClusterableDataSet.Patterns.
MeasurementSuite is essentially a collection of measurements.Comparable interface for a minimisation problem.MinimisationFitness with the given fitness value.
Diversity of the population.PSOs the pBest and lBest (or gBest) particles are replaced with the
concept of local and global guides respectively.CompositeMeasurement and Fitness
should be used insteadCompositeMeasurement and Solution
should be used insteadscalar with each component in the Vector.
scalar with each component in the Vector.
MultiPopulationBasedAlgorithm
(like VEPSO) is used to solve a Multi-objective problem.MultistartOptimisationAlgorithm is simply a wrapper.degree.
GuideSelectionStrategy where the neighbourhood
best position of a particle gets selected as a guide (usually global guide).Iterator over all particles in the neighbourhood of
the particle referred to by the given Iterator.
LBestTopology.neighbourhoodSize by updating the
ControlParameter and then construct a new iterator to be returned.
Iterator over all particles in the neighbourhood of
the particle referred to by the given Iterator.
Iterator over all particles in the neighbourhood of
the particle referred to by the given Iterator.
Algorithm instances.Object based on the underlying XML object description.
MultiPopulationCriterionBasedAlgorithm to assign the different sub-objectives
in a MOOptimisationProblem to specific PopulationBasedAlgorithms.OptimisationProblem.OptimisationSolution.
ReevaluationReactionStrategyGuideSelectionStrategy where the personal
best position of a particle gets selected as a guide (usually local guide).Type element if
it is no longer within the valid search space.OptimisationProblem into sub-problems of equal size/dimension.CentroidsInitialisationStrategy.
TopologyHolder.
TopologyHolder.
TopologyHolder.
TopologyHolder.
TopologyHolder.
TopologyLoopingOperator by looping over the topology and
delegate the operation to the wrapped operator.
TopologyHolder.
TopologyHolder.
TopologyHolder.
TopologyHolder.
TopologyHolder.
TopologyHolder.
entities inside the topology.
entities inside the topology.
Vector to another will result in a resultant Vector.
Vector to another will result in a resultant Vector.
Visitor to make it compatible
with Post-Order Traversals in the Containers defined.Visitor to make it compatible with Pre-Order
Traversals in the Containers defined.list to the beginning
of the current Vector.
Problem must satisfy this interface.ProportionalControlParameter instance.
MersenneTwister.
Random.
PSO.
Blackboard.
CurrentToRandCreationStrategy.
Algorithms in a random order.RandomAlgorithmIterator.
RandomAlgorithmIterator for the supplied list.
RandomAlgorithmIterator from the supplied one.
Bit object.
Vector.
Random.
Random.
Random.
Real object based on the upper and lower bounds.
RandomizingControlParameter instance.
RandomNumber instance.
MOOptimisationProblem to different
PopulationBasedAlgorithms.Random as the generator
class to use.
Random.
number of sentry entities and an value to
detect whether a change has occured in the environment.RandomSentriesDetectionStrategynumber of sentry points and an value to
detect whether a change has occured in the environment within a number of
consecutive iterations.Comparator.
seed
value.
Real instance with the initial value which is random between lower and upper.
ratio of randomly chosen entities in the given
Topology.ratio of randomly chosen entities in the given
Topology.element found within the Structure.
index.
element found within the Structure.
index.
element found within the Structure.
index.
element found within the Structure.
index.
element found within the Structure.
index.
element found within the Structure.
index.
element found within the Structure.
index.
index from the Vector.
structure, if contained.
structure, if contained.
structure, if contained.
structure, if contained.
structure, if contained.
structure.
Resetable instance.
InferiorFitness.
marker as the pivot point.
RNAParticle instance.
particle.
Selection is an abstraction that allows operations to be applied to
a collection instace that result in a selection of list elements, based on a varied of
potential combination of operators.KnowledgeTransferStrategy where two Selection
instances are used to first select a sub-population (PopulationBasedAlgorithm) from
a collection of population-based algorithms (see MultiPopulationBasedAlgorithm) and then
within this sub-population's {@link Topology}, which entity's knowledge is to be transfered
to the caller requesting it.Algorithms in a sequential order.SequentialAlgorithmIterator.
SequentialAlgorithmIterator for the supplied list.
SequentialAlgorithmIterator from the supplied one.
MOOptimisationProblem to different
PopulationBasedAlgorithm instances.index.
index to the true / on state.
Object at a point (row, column) within the Matrix.
index with element.
index.
index.
index.
Numeric type at the specified index.
alpha.
Archive implementation.index to the specified value.
Bit instance.
Bit instance.
CandidateSolution represents.
CandidateSolution represents.
ChangeStrategy for this problem.
ClusterableDataSets.
ClusteringProblems.
ControlParameter.
ControlParameter can be used to control the MinimumDiversity.consecutiveIterations value.
DataSetBuilder for this
optimistion problem.
DataSetManager singleton to parse and/or retrieve the given
DataSetBuilder.
DataSetBuilder for this
optimistion problem.
DataSetBuilder for this
optimistion problem.
String} that should be used as delimiter to
split a string into the elements of the pattern.
DistanceMeasure that will be used for all distance calculations
throughout a clustering.
Diversity hierarchy and its strategies.
FitnessCalculator for the current Entity.
id for this Topology.
id for this Topology.
index to the specified value.
IterationStrategy to be used.
M.
M.
ControlParameter can be used to control the MinimumDiversity.minimumDiversity value.
NeighbourhoodBestUpdateStrategy to be used by the Entity.
NeighbourhoodBestUpdateStrategy to be used by the Entity.
Entity.
Blackboard defining the properties of the CandidateSolution.
Blackboard defining the properties of the CandidateSolution.
Entity.
index to the specified value.
rho.
rho.
Measurement based on the provided instance.
velocity damping factor.
weight value for the current Entry
within the Selection.
problem instance.
Sigmoid.
SigmoidSimulationException without detail message.
SimulationException with the specified detail message.
BitArray
The result returned will be the size + 1.
Vector.
SocialEntity.SocialEntity instances, based on the available social best
fitness.Archive or selected to be used as guide during the search process.comparator as the
comparator to use.
SpecializedPopluationInitialisationStrategy.
Spherical.
OptimisationProblem into sub-problems and assigns all the sub-problems to Algorithms in the population.
OptimisationProblem into sub-problems.GuideUpdateStrategy in that it always updates the
given particle's guide.StandardPositionUpdateStrategy.
Step function.
fromIndex to toIndex.
Vector from the current Vector.
Vector from the current Vector.
Object [] from this Vector.
Object [] from this Vector.
Vector.
Topology
at once.String representation, using the provided delimiter.
String representation of the Real object.
String of the decorated element.
Type interface for all type-objects that are used within CIlib.Type instances.seedGenerator method is called.particle's guide (either local or global depending on guideType)
should be updated, and updates it with newGuide.
Particle.
Vector from vector Vector with each component's value
set to the upper bound of that component.
i and j.
Vector.
Vector with size as the initial
capacity.
Vector instance of the provided size, with
cloned copies of numeric.
Vector which is a copy of the provided instance.
Topology.
Topology.
Topology.
domainElement
and any possible repeats.
VonNeumannTopology.
XMLObjectFactory can be used to manage the construction of any object
based on an XML description.XMLObjectFactory for constructing objects
given an XML description.
XMLObjectFactory for constructing objects
given an XML description and an XML document for handling idrefs.
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