DPGA: a Simple Distributed Population Approach to Tackle Uncertainty

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Abstract

Evolutionary algorithms (EA) have been widely accepted as efficient optimizers for complex real life problems [2]. However, many real life optimization problems involve time-variant noisy environment, which pose major challenges to EA-based optimization. Presence of noise interferes with the evaluation and the selection process of EA and adversely affects the performance of the algorithm [6]. Also presence of noise means fitness function can not be evaluated and it has to be estimated instead. Several approaches have been tried to overcome this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory) [5]. In this paper we propose a method, DPGA (distributed population genetic algorithm) that uses a distributed population based architecture to simulate a distributed, self-adaptive memory of the solution space. Local regression is used in each sub-population to estimate the fitness. Specific problem category considered is that of optimization of functions with time variant noisy fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both adaptability and accuracy.
Original languageEnglish
Title of host publicationIEEE Congress on Evolutionary Computation
Place of PublicationUSA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages4061-4065
Number of pages5
ISBN (Electronic)9781424418220
DOIs
Publication statusPublished - 2008
EventCEC 2008 World Conference - Hong Kong, Hong Kong
Duration: 01 Jun 200806 Jun 2008

Conference

ConferenceCEC 2008 World Conference
CountryHong Kong
Period01/06/0806/06/08

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