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• W2020320008 endingPage "49" @default.
• W2020320008 startingPage "32" @default.
• W2020320008 abstract "A multiobjective optimization problem involves several conflicting objectives and has a set of Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary algorithms (MOEAs) are able to approximate the Pareto optimal set in a single run. MOEAs have attracted a lot of research effort during the last 20 years, and they are still one of the hottest research areas in the field of evolutionary computation. This paper surveys the development of MOEAs primarily during the last eight years. It covers algorithmic frameworks such as decomposition-based MOEAs (MOEA/Ds), memetic MOEAs, coevolutionary MOEAs, selection and offspring reproduction operators, MOEAs with specific search methods, MOEAs for multimodal problems, constraint handling and MOEAs, computationally expensive multiobjective optimization problems (MOPs), dynamic MOPs, noisy MOPs, combinatorial and discrete MOPs, benchmark problems, performance indicators, and applications. In addition, some future research issues are also presented." @default.
• W2020320008 created "2016-06-24" @default.
• W2020320008 creator A5000104803 @default.
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• W2020320008 creator A5050248676 @default.
• W2020320008 creator A5083869917 @default.
• W2020320008 date "2011-03-01" @default.
• W2020320008 modified "2023-10-14" @default.
• W2020320008 title "Multiobjective evolutionary algorithms: A survey of the state of the art" @default.
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