Handling Uncertainty with a Real-coded EA

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Presence of uncertainty in the search environment of Evolutionary algorithms (EA) interferes with the evaluation and the selection process of EA and adversely affects the performance of the algorithm. Presence of noise also means fitness function can not be evaluated and it has to be estimated instead. Of the various approaches which been tried to handle uncertainty in EA search environment, the more familiar approaches are: introduction of diversity (hyper mutation, random immigrants, special operators); and incorporation of memory of the past (diploidy, case based memory) [6]. In [2], we proposed a method, DPGA (distributed population evolutionary algorithm) that uses a distributed population architecture to simulate a distributed, self-adaptive memory of the solution space. Local regression is used in each sub-population to estimate the fitness. In the current research, we further investigate performance of DPGA for noisy fitness function i.e. fitness of any solution is altered by the addition of a noise term . 'Noisy' versions of few standard benchmark problems have been considered in the simulation runs of the DPGA algorithm.
Original languageEnglish
Title of host publicationGECCO 2008
EditorsKeijzer Maarten
Place of PublicationUSA
PublisherACM Press
Number of pages6
ISBN (Electronic)9781605581316
Publication statusPublished - 2008
EventGenetic and Evolutionary Computation Conference - USA, New Zealand
Duration: 12 Jul 200816 Jul 2008


ConferenceGenetic and Evolutionary Computation Conference
Country/TerritoryNew Zealand


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