Scaling Up evolutionary programming algorithms

Xin YAO, Yong LIU

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

25 Citations (Scopus)

Abstract

Most anMytical and experimental results on evolutionary programming (EP) are obtained using low-dimensional problems, e.g., smaller than 50. It is unclear, however, whether the empirical results obtained from the low-dimensional problems still hold for high-dimensional cases. This paper investigates the behaviour of four different EP algorithms for large-scale problems, i.e., problems whose dimension ranges from 100 to 300. The four are classical EB (CEP) [1, 2], fast EP (FEP) [3], improved FEP (IFEP) [4] and a mixed EP (MEP) proposed in this paper. It is discovered that neither CEP nor FEP performs satisfactorily for the large-scale problems investigated here. However, IFEP and MEP are able to perform consistently well for both unimodal and multimodal functions with various dimensionalities. In addition, the time used by IFEP and MEP to find a near optimal solution appears to grow only polynomially (second-order polynomial) as the dimensionality of the problems studied increases. © Springer-Verlag Berlin Heidelberg 1998.
Original languageEnglish
Title of host publicationEvolutionary Programming VII : 7th International Conference, EP98, San Diego, California, USA, March 25–27, 1998 Proceedings
EditorsV. W. PORTO, N. SARAVANAN, D. WAAGEN, A. E. EIBEN
PublisherSpringer Berlin Heidelberg
Pages103-112
Number of pages10
Volume1447
ISBN (Electronic)9783540685159
ISBN (Print)9783540648918
DOIs
Publication statusPublished - 1998
Externally publishedYes

Publication series

NameEP98: International Conference on Evolutionary Programming
PublisherSpringer, Berlin, Heidelberg
Volume1447
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

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