Performance of an ensemble of ordinary, universal, non-stationary and limit kriging predictors
Performance of an ensemble of ordinary, universal, non-stationary and limit kriging predictors
The selection of stationary or non-stationary Kriging to create a surrogate model of a black box function requires a priori knowledge of the nature of response of the function as these techniques are better at representing some types of responses than others. While an adaptive technique has been previously proposed to adjust the level of stationarity within the surrogate model such a model can be prohibitively expensive 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 functions and engineering design problems.
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
1 June 2013
Toal, David J.J.
dc67543d-69d2-4f27-a469-42195fa31a68
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
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).
Abstract
The selection of stationary or non-stationary Kriging to create a surrogate model of a black box function requires a priori knowledge of the nature of response of the function as these techniques are better at representing some types of responses than others. While an adaptive technique has been previously proposed to adjust the level of stationarity within the surrogate model such a model can be prohibitively expensive 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 functions and engineering design problems.
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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
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Date deposited: 29 Nov 2012 15:13
Last modified: 15 Mar 2024 03:29
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