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Evolutionary optimization for computationally expensive problems using Gaussian processes

Evolutionary optimization for computationally expensive problems using Gaussian processes
Evolutionary optimization for computationally expensive problems using Gaussian processes
The use of statistical models to approximate detailed analysis codes for evolutionary optimization has attracted some attention [1-3]. However, those early methodologies do suffer from some limitations, the most serious of which being the extra tuning parameter introduceds. Also the question of when to include more data points to the approximation model during the search remains unresolved. Those limitations might seriously impede their successful application. We present here an approach that makes use of the extra information provided by a Gaussian processes (GP) approximation model to guide the crucial model update step. We present here the advantages of using GP over other neural-net biologically inspired approaches. Results are presented for a real world-engineering problem involving the structural optimization of a satellite boom.
evolutionally computation, optimization, gaussian processes, computationally expensive problems
708-714
CSREA Press
El-Beltagy, Mohammed A.
814b4ceb-49ed-4783-aa62-1ed5d8cd66a1
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def
El-Beltagy, Mohammed A.
814b4ceb-49ed-4783-aa62-1ed5d8cd66a1
Keane, Andy J.
26d7fa33-5415-4910-89d8-fb3620413def

El-Beltagy, Mohammed A. and Keane, Andy J. (2001) Evolutionary optimization for computationally expensive problems using Gaussian processes. In Proceedings of the International Conference on Artificial Intelligence (IC-AI'2001). CSREA Press. pp. 708-714 .

Record type: Conference or Workshop Item (Paper)

Abstract

The use of statistical models to approximate detailed analysis codes for evolutionary optimization has attracted some attention [1-3]. However, those early methodologies do suffer from some limitations, the most serious of which being the extra tuning parameter introduceds. Also the question of when to include more data points to the approximation model during the search remains unresolved. Those limitations might seriously impede their successful application. We present here an approach that makes use of the extra information provided by a Gaussian processes (GP) approximation model to guide the crucial model update step. We present here the advantages of using GP over other neural-net biologically inspired approaches. Results are presented for a real world-engineering problem involving the structural optimization of a satellite boom.

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More information

Published date: 2001
Venue - Dates: International Conference on Artificial Intelligence (IC-AI'2001), 2001-06-25 - 2001-06-28
Keywords: evolutionally computation, optimization, gaussian processes, computationally expensive problems

Identifiers

Local EPrints ID: 21843
URI: https://eprints.soton.ac.uk/id/eprint/21843
PURE UUID: 5f2d73a0-b7fd-49a1-af58-67f53714a0f3

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Date deposited: 28 Feb 2007
Last modified: 17 Jul 2017 16:24

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Contributors

Author: Mohammed A. El-Beltagy
Author: Andy J. Keane

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