Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/9637
Title: Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems
Authors: Yang, S
Keywords: Distributed algorithms;Dynamic programming;Evolutionary computation;Storage management
Issue Date: 2005
Publisher: IEEE
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.
URI: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1555015
http://bura.brunel.ac.uk/handle/2438/9637
DOI: http://dx.doi.org/10.1109/CEC.2005.1555015
ISBN: 0-7803-9363-5
Appears in Collections:Dept of Computer Science Research Papers

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