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IEEE World Congress on Computational Intelligence

WCCI 2008: Computational Intelligence: Research Frontiers pp 262–292Cite as

Handling Uncertainties in Evolutionary Multi-Objective Optimization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5050))

Abstract

Evolutionary algorithms are stochastic search methods that are efficient and effective for solving sophisticated multi-objective (MO) problems. Advances made in the field of evolutionary multi-objective optimization (EMO) are the results of two decades worth of intense research, studying various topics that are unique to MO optimization. However many of these studies assume that the problem is deterministic and static, and the EMO performance generally deteriorates in the presence of uncertainties. In certain situations, the solutions found may not even be implementable in practice. In this chapter, the challenges faced in handling three different forms of uncertainties in EMO will be discussed, including 1) noisy objective functions, 2) dynamic MO fitness landscape, and 3) robust MO optimization. Specifically, the impact of these uncertainties on MO optimization will be described and the approaches/modifications to basic algorithm design for better and robust EMO performance will be presented.

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  1. National University of Singapore, 4 Engineering Drive 3, Singapore, 117576

    Kay Chen Tan

  2. Data Storage Institute, Agency for Science, Technology and Research, 5 Engineering Drive 1, Singapore, 117608

    Chi Keong Goh

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  1. Kay Chen Tan
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Jacek M. Zurada Gary G. Yen Jun Wang

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Tan, K.C., Goh, C.K. (2008). Handling Uncertainties in Evolutionary Multi-Objective Optimization. In: Zurada, J.M., Yen, G.G., Wang, J. (eds) Computational Intelligence: Research Frontiers. WCCI 2008. Lecture Notes in Computer Science, vol 5050. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68860-0_13

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