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Title: Evolutionary multi-objective optimization in uncertain environments
Keywords: Evolutionary algorithm, multiobjective, uncertainty
Issue Date: 26-Oct-2007
Citation: GOH CHI KEONG (2007-10-26). Evolutionary multi-objective optimization in uncertain environments. ScholarBank@NUS Repository.
Abstract: Evolutionary algorithms are stochastic search methods that are very efficient and effective in solving sophisticated multiobjective problems. While the majority of the optimization literature assumes that the problem is deterministic, most applications are characterized by various forms of uncertainties. Therefore, any solution found based on such an assumption may not be implementable in practice. The primary motivation of this work is to provide a comprehensive treatment on the design of evolutionary algorithms for multiobjective optimization with uncertainties. This work is divided into three parts. The first part addresses the issues of noisy fitness functions and noise-handling mechanisms are developed to improve algorithmic performance. The second part is concerned with dynamic multiobjective optimization and the notion of coevolution is extended to track the time-varying landscape. The final part of this work is focused on robustness where the optimality of the solutions is sensitive to parameter variations.
Appears in Collections:Ph.D Theses (Open)

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