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|Title:||Handling uncertainties in evolutionary multi-objective optimization|
|Authors:||Tan, K.C. |
|Source:||Tan, K.C.,Goh, C.K. (2008). Handling uncertainties in evolutionary multi-objective optimization. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5050 LNCS : 262-292. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-540-68860-0_13|
|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. © 2008 Springer-Verlag Berlin Heidelberg.|
|Source Title:||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Appears in Collections:||Staff Publications|
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