Optimization with missing data

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


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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
ePrint ID: 23505
Date :
Date Event
Date Deposited: 13 Mar 2006
Last Modified: 16 Apr 2017 22:45
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/23505

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