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- W2045984843 abstract "In multiobjective optimization problems, Pareto dominance-based search techniques are known to lose their efficiency in problems with a large number of objective functions - the many-objective problems. This paper proposes an algorithm based on a stochastic differential equation approach combined with an evolutionary strategy for dealing with such problems. The proposed algorithm is intended to both allow the determination of tight Pareto-optimal solutions in many-objective problems (which is a difficult task for usual evolutionary algorithms) and to find a solution set that performs a relatively uniform sampling of the Pareto-optimal set (which is a deficiency of the known stochastic differential equation approach). The proposed algorithm is shown to attain such goals at a relatively low computational cost." @default.
- W2045984843 created "2016-06-24" @default.
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- W2045984843 date "2012-06-01" @default.
- W2045984843 modified "2023-10-16" @default.
- W2045984843 title "A CMA stochastic differential equation approach for many-objective optimization" @default.
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- W2045984843 doi "https://doi.org/10.1109/cec.2012.6253014" @default.
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