Optimization with missing data

Forrester, Alexander I.J., Sobester, Andras and Keane, Andy J. (2006) Optimization with missing data. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 462, (2067), 935-945. (doi:10.1098/rspa.2005.1608).


Download (1859Kb)
Original Publication URL: http://dx.doi.org/10.1098/rspa.2005.1608


Engineering optimization relies routinely on deterministic computer based design evaluations, typically comprising geometry creation, mesh generation and numerical simulation. Simple optimization routines tend to stall and require user intervention if a failure occurs at any of these stages. This motivated us to develop an optimization strategy based on surrogate modelling, which penalizes the likely failure regions of the design space without prior knowledge of their locations. A Gaussian process based design improvement expectation measure guides the search towards the feasible global optimum.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1098/rspa.2005.1608
ISSNs: 1364-5021 (print)
Related URLs:
Keywords: global optimization, imputation, kriging
Subjects: T Technology > T Technology (General)
Q Science > QA Mathematics
Divisions : University Structure - Pre August 2011 > School of Engineering Sciences
ePrint ID: 23505
Accepted Date and Publication Date:
Date Deposited: 13 Mar 2006
Last Modified: 31 Mar 2016 11:44
URI: http://eprints.soton.ac.uk/id/eprint/23505

Actions (login required)

View Item View Item

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