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Surrogate-assisted co-evolutionary search

Surrogate-assisted co-evolutionary search
Surrogate-assisted co-evolutionary 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.
1140-1145
Ong, Y.S.
62497a6f-823e-4663-b263-4a805a00f181
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Nair, P.B.
da7138d7-da7f-45af-887b-acc1d0e77a6f
Ong, Y.S.
62497a6f-823e-4663-b263-4a805a00f181
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Nair, P.B.
da7138d7-da7f-45af-887b-acc1d0e77a6f

Ong, Y.S., Keane, A.J. and Nair, P.B. (2002) Surrogate-assisted co-evolutionary search. Proceedings of the 4th Asia Pacific Conference on Simulated Evolution and Learning, Singapore. 01 Nov 2002. pp. 1140-1145 .

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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More information

Published date: 2002
Venue - Dates: Proceedings of the 4th Asia Pacific Conference on Simulated Evolution and Learning, Singapore, 2002-11-01 - 2002-11-01

Identifiers

Local EPrints ID: 22250
URI: http://eprints.soton.ac.uk/id/eprint/22250
PURE UUID: 6c27de0d-2790-46f7-bc44-8f42156dc8fe

Catalogue record

Date deposited: 02 Jun 2006
Last modified: 22 Jul 2020 16:42

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Contributors

Author: Y.S. Ong
Author: A.J. Keane
Author: P.B. Nair

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