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|Title:||Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems|
|Keywords:||Distributed algorithms;Dynamic programming;Evolutionary computation;Storage management|
|Citation:||IEEE, 3 pp. 2560 - 2567, 2005|
|Abstract:||Several approaches have been developed into evolutionary algorithms to deal with dynamic optimization problems, of which memory and random immigrants are two major schemes. This paper investigates the application of a direct memory scheme for univariate marginal distribution algorithms (UMDAs), a class of evolutionary algorithms, for dynamic optimization problems. The interaction between memory and random immigrants for UMDAs in dynamic environments is also investigated. Experimental study shows that the memory scheme is efficient for UMDAs in dynamic environments and that the interactive effect between memory and random immigrants for UMDAs in dynamic environments depends on the dynamic environments.|
|Appears in Collections:||Dept of Computer Science Research Papers|
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