See: Description
Interface | Description |
---|---|
EpsilonAdaptation |
The
EpsilonAdaptation manages the adaptation of the ε-value stored in
the AdaptiveEpsilon . |
EpsilonMapping |
The
EpsilonMapping implements the ε mapping used by the
EpsilonSamplingSelector . |
ESamplingSurvivorGeneration |
The
ESamplingSurvivorGeneration generates the survivor pool during
the selection implemented by EpsilonSamplingSelector . |
NeighborhoodScheduler |
The
NeighborhoodScheduler manages the schedule according to which the
neighborhoods are chosen by the EpsilonNeighborhoodCoupler to pick the crossover
parents. |
Class | Description |
---|---|
AdaptiveEpsilon |
The
AdaptiveEpsilon contains the information about an ε-value and the
information that is used by the EpsilonAdaptation to adapt the
ε-value. |
AeSeHModule |
The
AeSeHModule binds the EpsilonSamplingSelector as
Selector and the EpsilonNeighborhoodCoupler as
Coupler and configures them with the parameters used in the AeSeH
paper (see the citation for details). |
EpsilonAdaptationDelta |
The
EpsilonAdaptationDelta adapts the ε values exactly as described
in the paper cited in the AeSeHModule . |
EpsilonMappingAdditive |
The
EpsilonMappingAdditive implements the evenly spaced adaptive ε
function. |
EpsilonNeighborhoodCoupler |
The
EpsilonNeighborhoodCoupler implements a parent selection process
based on by the ε-neighborhood. |
EpsilonNeighborhoodCouplerModule |
Binds the
EpsilonNeighborhoodCoupler as the Coupler . |
EpsilonSamplingSelector |
The
EpsilonSamplingSelector implements a selection process based on
the ε-sampling. |
EpsilonSamplingSelectorModule |
Binds the
EpsilonSamplingSelector as Selector . |
ESamplingSurvivorGenerationBasic |
The
ESamplingSurvivorGenerationBasic implements the basic survivor
selection used by the Adaptive ε-sampling and ε-hood for evolutionary
many-objective optimization. |
NeighborhoodSchedulerRoundRobin |
The
NeighborhoodSchedulerRoundRobin schedules the neighborhoods in a simple
round-robin fashion. |
Package for the classes of the Adaptive ε-Sampling ε-Hood MOEA. This evolutionary algorithm performs both the survivor and the parent selection based on ε-sampling.
The EpsilonSamplingSelector
uses the
ESamplingSurvivorGeneration
to pick the
individuals that form the pool of possible parents. Hereby, ε-dominant
Individual
s are preferred. The check for ε-dominance
is implemented by enhancing the Objectives
of the
considered individual using the
EpsilonMapping
. For the parent
selection, the EpsilonNeighborhoodCoupler
divides the
survivor pool based on ε-dominance among the survivors. A pair of parents is
then always picked from the same neighborhood, while the arbitration of the
neighborhoods to pick from is handled by the
NeighborhoodScheduler
.
Throughout the exploration, the ε-values used for the survivor generation and
the parent selection are adjusted in order to pick a number of ε-dominant
survivors that equals the number of individuals that are to be generated in
each generation and to create a number of neighborhoods close to a number
provided by the user. The adaptation of the ε-values is hereby managed by the
EpsilonAdaptation
.
In the Opt4J GUI, the AeSeH is configured using the
AeSeHModule
and the
EvolutionaryAlgorithmModule
.