Surrogate-assisted coevolutionary search
Surrogate-assisted coevolutionary search
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.
9810475241
1140-1145
Ong, Y.S.
62497a6f-823e-4663-b263-4a805a00f181
Keane, A. J.
26d7fa33-5415-4910-89d8-fb3620413def
Nair, P.B.
d4d61705-bc97-478e-9e11-bcef6683afe7
2002
Ong, Y.S.
62497a6f-823e-4663-b263-4a805a00f181
Keane, A. J.
26d7fa33-5415-4910-89d8-fb3620413def
Nair, P.B.
d4d61705-bc97-478e-9e11-bcef6683afe7
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.
IEEE.
.
Record type:
Conference or Workshop Item
(Paper)
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.
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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
Identifiers
Local EPrints ID: 23163
URI: http://eprints.soton.ac.uk/id/eprint/23163
ISBN: 9810475241
PURE UUID: d350cf55-29c4-4ee1-a019-c132c9f9e993
Catalogue record
Date deposited: 05 Jun 2006
Last modified: 06 Mar 2024 02:36
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Contributors
Author:
Y.S. Ong
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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