Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2007.4424521
Title: Noise-induced features in robust multi-objective optimization problems
Authors: Goh, C.K.
Tan, K.C. 
Cheong, C.Y.
Ong, Y.S.
Keywords: Evolutionary algorithms
Multi-objective optimization
Robust solutions
Robust test functions
Issue Date: 2007
Citation: Goh, C.K., Tan, K.C., Cheong, C.Y., Ong, Y.S. (2007). Noise-induced features in robust multi-objective optimization problems. 2007 IEEE Congress on Evolutionary Computation, CEC 2007 : 568-575. ScholarBank@NUS Repository. https://doi.org/10.1109/CEC.2007.4424521
Abstract: Apart from the need to satisfy several competing objectives, many real-world applications are also sensitive to decision or environmental parameter variation which results in large or unacceptable performance variation. While evolutionary optimization techniques have several advantages over operational research methods for robust optimization, it is rarely studied by the evolutionary multi-objective (MO) optimization community. This paper addresses the issue of robust MO optimization by presenting a robust continuous MO test suite with features of noise-induced solution space, fitness landscape and decision space variation. The work presented in this paper should encourage further studies and the development of more effective algorithms for robust MO optimization. © 2007 IEEE.
Source Title: 2007 IEEE Congress on Evolutionary Computation, CEC 2007
URI: http://scholarbank.nus.edu.sg/handle/10635/71141
ISBN: 1424413400
DOI: 10.1109/CEC.2007.4424521
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