Uncertainty and Evolutionary Optimization: A Novel Approach

Research output: Book chapter/Published conference paperConference paperpeer-review

8 Citations (Scopus)


Evolutionary algorithms (EA) have been widely accepted as efficient solvers for complex real world optimization problems, including engineering optimization. However, real world optimization problems often involve uncertain environment including noisy and/or dynamic environments, which pose major challenges to EA-based optimization. The presence of noise interferes with the evaluation and the selection process of EA, and thus adversely affects its performance. In addition, as presence of noise poses challenges to the evaluation of the fitness function, it may need to be estimated instead of being evaluated. Several existing approaches attempt to address this problem, such as introduction of diversity (hyper mutation, random immigrants, special operators) or incorporation of memory of the past (diploidy, case based memory). However, these approaches fail to adequately address the problem. In this paper we propose a Distributed Population Switching Evolutionary Algorithm (DPSEA) method that addresses optimization of functions with noisy fitness using a distributed population switching architecture, to simulate a distributed self-adaptive memory of the solution space. Local regression is used in the pseudo-populations to estimate the fitness. Successful applications to benchmark test problems ascertain the proposed method's superior performance in terms of both robustness and accuracy.
Original languageEnglish
Title of host publicationProceedings of the 2014 9th IEEE Conference on Industrial Electronics and Applications
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages6
Publication statusPublished - 2014
EventThe 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014) - Zhijiang Hotel, Hangzhou, China
Duration: 09 Jun 201411 Jun 2014


ConferenceThe 9th IEEE Conference on Industrial Electronics and Applications (ICIEA 2014)
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