Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/71139
Title: Noise handling in evolutionary multi-objective optimization
Authors: Goh, C.K.
Tan, K.C. 
Issue Date: 2006
Source: Goh, C.K.,Tan, K.C. (2006). Noise handling in evolutionary multi-objective optimization. 2006 IEEE Congress on Evolutionary Computation, CEC 2006 : 1354-1361. ScholarBank@NUS Repository.
Abstract: In addition to the need to satisfy several competing objectives, many real-world applications are also characterized by noise. In this paper, three noise-handling features, an experiential learning directed perturbation (ELDP) operator, a gene adaptation selection strategy (GASS) and a possibilistic archiving model are proposed. The ELDP adapts the magnitude and direction of variation according to past experiences for fast convergence while the GASS improves the evolutionary search in escaping from premature convergence in both noiseless and noisy environments. The possibilistic archiving model is 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 three benchmark problems characterized by different difficulties. © 2006 IEEE.
Source Title: 2006 IEEE Congress on Evolutionary Computation, CEC 2006
URI: http://scholarbank.nus.edu.sg/handle/10635/71139
ISBN: 0780394879
Appears in Collections:Staff Publications

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