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


Toal, David J.J. and Keane, A.J. (2012) Performance of an ensemble of ordinary, universal, non-stationary and limit kriging predictors Structural and Multidisciplinary Optimization, 47, (6)

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Description/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.

Item Type: Article
ISSNs: 1615-147X (print)
Related URLs:
Subjects:
Organisations: Computational Engineering & Design Group
ePrint ID: 345732
Date :
Date Event
29 November 2012Accepted/In Press
1 June 2013Published
Date Deposited: 29 Nov 2012 15:13
Last Modified: 17 Apr 2017 16:19
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/345732

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