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Hierarchical surrogate-assisted evolutionary optimization framework

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 Institute of Electrical and Electronics Engineers pp. 1586-1593. (doi:10.1109/CEC.2004.1331085).

Record type: Book Section


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.

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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


Local EPrints ID: 22967
PURE UUID: 9746024a-e724-49af-bc76-02f84a60682a

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Date deposited: 03 Apr 2006
Last modified: 17 Jul 2017 16:19

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Author: Zongzhao Zhou
Author: Yew Soon Ong
Author: P.B. Nair

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