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).

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Original Publication URL: http://dx.doi.org/10.1098/rspa.2005.1608

Description/Abstract

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
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
Date Deposited: 13 Mar 2006
Last Modified: 27 Mar 2014 18:12
Contact Email Address: alexander.forrester@soton.ac.uk
URI: http://eprints.soton.ac.uk/id/eprint/23505

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