DocumentCode :
2732035
Title :
Memory-enhanced univariate marginal distribution algorithms for dynamic optimization problems
Author :
Yang, Shengxiang
Author_Institution :
Dept. of Comput. Sci., Leicester Univ., UK
Volume :
3
fYear :
2005
fDate :
2-5 Sept. 2005
Firstpage :
2560
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.
Keywords :
distributed algorithms; dynamic programming; evolutionary computation; storage management; UMDA; direct memory scheme; dynamic optimization problems; evolutionary algorithms; memory immigrants; random immigrants; univariate marginal distribution algorithms; Application software; Computer science; Design optimization; Electric breakdown; Electronic design automation and methodology; Evolutionary computation; Genetic algorithms; Heuristic algorithms; Mathematical analysis; Testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2005. The 2005 IEEE Congress on
Print_ISBN :
0-7803-9363-5
Type :
conf
DOI :
10.1109/CEC.2005.1555015
Filename :
1555015
Link To Document :
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