Metalmodeling techniques for evolutionary optimization of computationally expensive problems: promises and limitations
Metalmodeling techniques for evolutionary optimization of computationally expensive problems: promises and limitations
It is often the case in many problems in science and engineering that the analysis codes used are computationally very expensive. This can pose a serious impediment to the successful application of evolutionary optimization techniques. Metamodeling techniques present an enabling methodology for reducing the computational cost of such optimization problems. We present here a general framework for coupling metamodeling techniques with evolutionary algorithms to reduce the computational burden of solving this class of optimization problems. This framework aims to balance the concerns of optimization with that of design of experiments. Experiments on test problems and a practical engineering design problem serve to illustrate our arguments. The practical limitations of this approach are also outlined.
196-203
El-Beltagy, M.A.
35c62da1-b637-4ad7-bfb1-4be877561dc0
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
1999
El-Beltagy, M.A.
35c62da1-b637-4ad7-bfb1-4be877561dc0
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
El-Beltagy, M.A. and Keane, A.J.
(1999)
Metalmodeling techniques for evolutionary optimization of computationally expensive problems: promises and limitations.
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), Orlando, USA.
13 - 17 Jul 1999.
.
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Conference or Workshop Item
(Paper)
Abstract
It is often the case in many problems in science and engineering that the analysis codes used are computationally very expensive. This can pose a serious impediment to the successful application of evolutionary optimization techniques. Metamodeling techniques present an enabling methodology for reducing the computational cost of such optimization problems. We present here a general framework for coupling metamodeling techniques with evolutionary algorithms to reduce the computational burden of solving this class of optimization problems. This framework aims to balance the concerns of optimization with that of design of experiments. Experiments on test problems and a practical engineering design problem serve to illustrate our arguments. The practical limitations of this approach are also outlined.
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elbe_99c.pdf
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Published date: 1999
Venue - Dates:
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-99), Orlando, USA, 1999-07-13 - 1999-07-17
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Local EPrints ID: 23624
URI: http://eprints.soton.ac.uk/id/eprint/23624
PURE UUID: 7d54a708-75af-40ea-a51e-f30a93453c1a
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Date deposited: 15 Feb 2007
Last modified: 16 Mar 2024 02:53
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Author:
M.A. El-Beltagy
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