Hierarchical surrogate-assisted evolutionary optimization framework
Hierarchical surrogate-assisted evolutionary optimization framework
This work presents enhancements to a surrogate-assisted evolutionary optimization framework proposed earlier in the literature for solving computationally expensive design problems on a limited computational budget (Ong et al., 2003). The main idea of our former framework was to couple evolutionary algorithms with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning, including a trust-region approach for interleaving the true fitness function with computationally cheap local surrogate models during gradient-based search. We propose a hierarchical surrogate-assisted evolutionary optimization framework for accelerating the convergence rate of the original surrogate-assisted evolutionary optimization framework. Instead of using the exact high-fidelity fitness function during evolutionary search, a Kriging global surrogate model is used to screen the population for individuals that undergo Lamarckian learning. Numerical results are presented for two multimodal benchmark test functions to show that the proposed approach leads to a further acceleration of the evolutionary search process.
evolutionary computation, gradient methods, learning (artificial intelligence), quadratic programming, search problems, statistical analysis
1586-1593
Zhou, Zongzhao
26d43391-276c-44d0-8745-7bdefd5b9dc3
Ong, Yew Soon
3e7a6a91-6eab-4ca6-81c5-c9f3ee20e2fb
Nair, P.B.
d4d61705-bc97-478e-9e11-bcef6683afe7
2004
Zhou, Zongzhao
26d43391-276c-44d0-8745-7bdefd5b9dc3
Ong, Yew Soon
3e7a6a91-6eab-4ca6-81c5-c9f3ee20e2fb
Nair, P.B.
d4d61705-bc97-478e-9e11-bcef6683afe7
Zhou, Zongzhao, Ong, Yew Soon and Nair, P.B.
(2004)
Hierarchical surrogate-assisted evolutionary optimization framework.
In,
Congress on evolutionary computing.
Congress on Evolutionary Computing 2004 (01/01/04)
IEEE, .
(doi:10.1109/CEC.2004.1331085).
Record type:
Book Section
Abstract
This work presents enhancements to a surrogate-assisted evolutionary optimization framework proposed earlier in the literature for solving computationally expensive design problems on a limited computational budget (Ong et al., 2003). The main idea of our former framework was to couple evolutionary algorithms with a feasible sequential quadratic programming solver in the spirit of Lamarckian learning, including a trust-region approach for interleaving the true fitness function with computationally cheap local surrogate models during gradient-based search. We propose a hierarchical surrogate-assisted evolutionary optimization framework for accelerating the convergence rate of the original surrogate-assisted evolutionary optimization framework. Instead of using the exact high-fidelity fitness function during evolutionary search, a Kriging global surrogate model is used to screen the population for individuals that undergo Lamarckian learning. Numerical results are presented for two multimodal benchmark test functions to show that the proposed approach leads to a further acceleration of the evolutionary search process.
Text
zhou_04.pdf
- Accepted Manuscript
More information
Published date: 2004
Additional Information:
CEC2004
Venue - Dates:
Congress on Evolutionary Computing 2004, 2004-01-01
Keywords:
evolutionary computation, gradient methods, learning (artificial intelligence), quadratic programming, search problems, statistical analysis
Identifiers
Local EPrints ID: 22967
URI: http://eprints.soton.ac.uk/id/eprint/22967
PURE UUID: 9746024a-e724-49af-bc76-02f84a60682a
Catalogue record
Date deposited: 03 Apr 2006
Last modified: 15 Mar 2024 06:42
Export record
Altmetrics
Contributors
Author:
Zongzhao Zhou
Author:
Yew Soon Ong
Author:
P.B. Nair
Download statistics
Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.
View more statistics