Surrogate-assisted coevolutionary search


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

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Description/Abstract

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.

Item Type: Book Section
Additional Information: INSPEC Accession Number: 7937198
ISBNs: 9810475241 (hardback)
Related URLs:
Subjects: T Technology
Divisions: University Structure - Pre August 2011 > School of Engineering Sciences
ePrint ID: 23163
Date Deposited: 05 Jun 2006
Last Modified: 27 Mar 2014 18:12
Publisher: Institute of Electrical and Electronics Engineers
URI: http://eprints.soton.ac.uk/id/eprint/23163

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