The University of Southampton
University of Southampton Institutional Repository

Evolutionary optimization of computationally expensive problems via surrogate modeling

Ong, Yew S., Nair, Prasanth B. and Keane, Andrew J. (2003) Evolutionary optimization of computationally expensive problems via surrogate modeling AIAA Journal, 41, (4), pp. 687-696.

Record type: Article


We present a parallel evolutionary optimization algorithm that leverages surrogate models for solving computationally expensive design problems with general constraints, on a limited computational budget. The essential backbone of our framework is an evolutionary algorithm coupled with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning.We employ a trust-region approach for interleaving use of exact models for the objective and constraint functionswith computationally cheap surrogatemodels during local search. In contrast to earlier work, we construct local surrogatemodels using radial basis functionsmotivated by the principle of transductive inference. Further, the present approach retains the intrinsic parallelism of evolutionary algorithms and can hence be readily implemented on grid computing infrastructures. Experimental results are presented for some benchmark test functions and an aerodynamic wing design problem to demonstrate that our algorithm converges to good designs on a limited computational budget.

PDF 22421.pdf - Version of Record
Restricted to Repository staff only
Download (1MB)

More information

Published date: 2003


Local EPrints ID: 22421
ISSN: 0001-1452
PURE UUID: 8e7d3136-a152-41dd-9e43-0015e7a71435

Catalogue record

Date deposited: 22 Mar 2006
Last modified: 17 Jul 2017 16:22

Export record


Author: Yew S. Ong
Author: Prasanth B. Nair
Author: Andrew J. Keane

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton:

ePrints Soton supports OAI 2.0 with a base URL of

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.