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CEC
2005
IEEE

Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems

10 years 1 months ago
Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems
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.
Shengxiang Yang
Added 24 Jun 2010
Updated 24 Jun 2010
Type Conference
Year 2005
Where CEC
Authors Shengxiang Yang
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