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Surrogate-assisted coevolutionary search

Ong, Y.S., Keane, A. J. and Nair, P.B., (2002) Surrogate-assisted coevolutionary search Wang, Lipo, Rajapakse, Jagath C., Fukushima, Kunihiko, Lee, Soo-Young and Yao, Xing (eds.) In Neural Information Processing, 2002. ICONIP '02. Proceedings of the 9th International Conference on. Institute of Electrical and Electronics Engineers., pp. 1140-1145.

Record type: Conference or Workshop Item (Paper)


This paper is concerned with an experimental evaluation of coevolutionary optimization techniques, which are integrated with surrogate models of the fitness function. The motivation for this study arises from the fact that since coevolutionary search is based on the divide-and-conquer paradigm, it may be possible to circumvent the 'curse of dimensionality' inherent in surrogate modeling techniques such as radial basis networks. We investigate the applicability of the algorithms presented in this paper to solve computationally expensive optimization problems on a limited computational budget via studies on a benchmark test function and a real world two-dimensional cantilevered space structure design problem. We show that by employing approximate models for the fitness, it becomes possible to converge to good solutions even for functions with a high degree of epistasis.

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Published date: 2002
Additional Information: INSPEC Accession Number: 7937198
Venue - Dates: ICONIP'02: 9th International Conference on Neural Information Processing, 2002-01-01


Local EPrints ID: 23163
ISBN: 9810475241
PURE UUID: d350cf55-29c4-4ee1-a019-c132c9f9e993

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Date deposited: 05 Jun 2006
Last modified: 17 Jul 2017 16:19

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Author: Y.S. Ong
Author: A. J. Keane
Author: P.B. Nair
Editor: Lipo Wang
Editor: Jagath C. Rajapakse
Editor: Kunihiko Fukushima
Editor: Soo-Young Lee
Editor: Xing Yao

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