A new hybrid updating scheme for an evolutionary search strategy using genetic algorithms and kriging
A new hybrid updating scheme for an evolutionary search strategy using genetic algorithms and kriging
This paper presents an efficient evolutionary search strategy based on design of experiments, genetic algorithms and response surface modelling. The strategy is constructed around a genetic algorithm while incorporating elements from design of experiment (DoE) and Kriging. In particular, the design points used to update the approximation model are derived from two surfaces, one is the approximation itself which provides the prediction of the function and the other is based on the error surface computed from posterior error estimates of the Kriging model. A genetic algorithm, which supports clustering, is used on both surfaces to return multiple points for parallel evaluation of the true function. A screening method is also used to remove points lying close to existing points based on the correlation coefficients between the point to be evaluated and all existing points. Numerical experiments suggest that significant improvements can be achieved using the proposed approach. Applications of the approach on engineering design problems are also studied.
1-8
American Institute of Aeronautics and Astronautics
Song, W.
390dc209-bfcb-4986-8362-c25b40272307
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
2005
Song, W.
390dc209-bfcb-4986-8362-c25b40272307
Keane, A.J.
26d7fa33-5415-4910-89d8-fb3620413def
Song, W. and Keane, A.J.
(2005)
A new hybrid updating scheme for an evolutionary search strategy using genetic algorithms and kriging.
In 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics and Materials Conference.
American Institute of Aeronautics and Astronautics.
.
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(Paper)
Abstract
This paper presents an efficient evolutionary search strategy based on design of experiments, genetic algorithms and response surface modelling. The strategy is constructed around a genetic algorithm while incorporating elements from design of experiment (DoE) and Kriging. In particular, the design points used to update the approximation model are derived from two surfaces, one is the approximation itself which provides the prediction of the function and the other is based on the error surface computed from posterior error estimates of the Kriging model. A genetic algorithm, which supports clustering, is used on both surfaces to return multiple points for parallel evaluation of the true function. A screening method is also used to remove points lying close to existing points based on the correlation coefficients between the point to be evaluated and all existing points. Numerical experiments suggest that significant improvements can be achieved using the proposed approach. Applications of the approach on engineering design problems are also studied.
Text
song_05a.pdf
- Accepted Manuscript
More information
Published date: 2005
Additional Information:
AIAA 2005-1901
Venue - Dates:
46th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics & Materials Conference, Austin, USA, 2005-04-18 - 2005-04-21
Identifiers
Local EPrints ID: 23898
URI: http://eprints.soton.ac.uk/id/eprint/23898
PURE UUID: 2827aec8-377a-41f9-ace5-15e56c08879e
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Date deposited: 24 Mar 2006
Last modified: 16 Mar 2024 02:53
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Contributors
Author:
W. Song
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