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Performance of an ensemble of ordinary, universal, non-stationary and limit kriging predictors

Toal, David J.J. and Keane, A.J. (2013) Performance of an ensemble of ordinary, universal, non-stationary and limit kriging predictors Structural and Multidisciplinary Optimization, 47, (6)

Record type: Article

Abstract

The selection of stationary or non-stationary
Kriging to create a surrogate model of a black box
function requires apriori knowledge of the nature of
response of the function as these techniques are bet-
ter at representing some types of responses than oth-
ers. While an adaptive technique has been previously
proposed to adjust the level of stationarity within the
surrogate model such a model can be prohibitively ex-
pensive to construct for high dimensional problems. An
alternative approach is to employ a surrogate model
constructed from an ensemble of stationary and non-
stationary Kriging models. The following paper assesses
the accuracy and optimization performance of such a
modelling strategy using a number of analytical func-
tions and engineering design problems.

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More information

Accepted/In Press date: 29 November 2012
Published date: 1 June 2013
Organisations: Computational Engineering & Design Group

Identifiers

Local EPrints ID: 345732
URI: http://eprints.soton.ac.uk/id/eprint/345732
ISSN: 1615-147X
PURE UUID: 1ca7430f-ffde-4ed1-8539-4329eb4ea3f5
ORCID for David J.J. Toal: ORCID iD orcid.org/0000-0002-2203-0302

Catalogue record

Date deposited: 29 Nov 2012 15:13
Last modified: 18 Jul 2017 05:08

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