Please use this identifier to cite or link to this item: https://doi.org/10.1109/TEVC.2006.882428
Title: An investigation on noisy environments in evolutionary multiobjective optimization
Authors: Goh, C.K.
Tan, K.C. 
Keywords: Evolutionary algorithms (EAs)
Multiobjective optimization
Noisy fitness function
Issue Date: Jun-2007
Source: Goh, C.K., Tan, K.C. (2007-06). An investigation on noisy environments in evolutionary multiobjective optimization. IEEE Transactions on Evolutionary Computation 11 (3) : 354-381. ScholarBank@NUS Repository. https://doi.org/10.1109/TEVC.2006.882428
Abstract: In addition to satisfying several competing objectives, many real-world applications are also characterized by a certain degree of noise, manifesting itself in the form of signal distortion or uncertain information. In this paper, extensive studies are carried out to examine the impact of noisy environments in evolutionary multiobjective optimization. Three noise-handling features are then proposed based upon the analysis of empirical results, including an experiential learning directed perturbation operator that adapts the magnitude and direction of variation according to past experiences for fast convergence, a gene adaptation selection strategy that helps the evolutionary search in escaping from local optima or premature convergence, and a possibilistic archiving model based on the concept of possibility and necessity measures to deal with problem of uncertainties. In addition, the performances of various multiobjective evolutionary algorithms in noisy environments, as well as the robustness and effectiveness of the proposed features are examined based upon five benchmark problems characterized by different difficulties in local optimality, nonuniformity, discontinuity, and nonconvexity. © 2006 IEEE.
Source Title: IEEE Transactions on Evolutionary Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/55042
ISSN: 1089778X
DOI: 10.1109/TEVC.2006.882428
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