Over-selection : An attempt to boost EDA under small population size

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review

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Detail(s)

Original languageEnglish
Title of host publication2007 IEEE Congress on Evolutionary Computation, CEC 2007
Pages1075-1182
Publication statusPublished - 2007

Conference

Title2007 IEEE Congress on Evolutionary Computation, CEC 2007
PlaceSingapore
Period25 - 28 September 2007

Abstract

Estimation of distribution algorithm (EDA) is a new class of evolutionary algorithms with a wide range of real-world applications. However, it has been well known that the performance of EDA is not satisfactory enough if its population size is small. But to simply increase its population size may result in slow convergence. To the best knowledge of the authors', very few work has been done on improving the performance of EDA under small population size. This paper illustrates why EDA does not work well under small population size and proposes a novel approach termed as Over-Selection to boost EDA under small population size. Experimental results on several benchmark problems demonstrate that Over-Selection based EDA is often able to achieve a better solution without significantly increasing its time consumption when compared with the original version of EDA. © 2007 IEEE.

Citation Format(s)

Over-selection: An attempt to boost EDA under small population size. / Hong, Yi; Kwong, Sam; Ren, Qingsheng et al.
2007 IEEE Congress on Evolutionary Computation, CEC 2007. 2007. p. 1075-1182 4424589.

Research output: Chapters, Conference Papers, Creative and Literary Works (RGC: 12, 32, 41, 45)32_Refereed conference paper (with host publication)peer-review