Reduced Computation for Evolutionary Optimization in Noisy Environment

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Evolutionary Algorithms' (EAs') application to real world optimization problems often involves expensive fitness function evaluation. Naturally this has a crippling effect on the performance of any population based search technique such as EA. Estimating the fitness of individuals instead of actually evaluating them is a workable approach to deal with this situation. Optimization problems in real world often involve expensive fitness. In [14] and [15] we presented two EA models, namely DAFHEA (Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm) and DAFHEA-II respectively. The original DAFHEA framework [14] reduces computation time by controlled use of meta-models generated by Support Vector Machine regression to partly replace actual fitness evaluation by estimation. DAFHEA-II [15] is an enhancement to the original framework in that it can be applied to problems that involve uncertainty. DAFHEA-II, incorporates a multiple-model based learning approach for the support vector machine approximator to filter out effects of noise [15]. In this paper we present further investigation on the performance of DAFHEA and DAFHEA-II.
Original languageEnglish
Title of host publicationGECCO 2008
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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