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- W22335698 abstract "A novel self-adaption strategy for the parameter epsilon in epsilon-MOEA is proposed in this paper based on the analyses of the relationship between the value of epsilon and the maximum number of non-dominated solutions. Then this novel strategy is applied in epsilon-MOEA and tested on 10 common benchmark functions. The experimental results demonstrate that even if without the good initial value for the parameter epsilon, epsilon-MOEA with this self-adaption strategy (named Algorithm 1) is able to approximately obtain the expected number of non-dominated solutions, which are very close to and uniformly distributed on the Pareto-optimal front. Furthermore, the genetic drift phenomenon in Algorithm 1 is discussed Two cases of genetic drift are pointed out, and one case can be fixed up by a simple approach proposed in this paper." @default.
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- W22335698 date "2008-01-01" @default.
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- W22335698 title "The Self-Adaption Strategy for Parameter epsilon in epsilon-MOEA" @default.
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