NDLI: Self-adaptive lower confidence bound: A new general and effective prescreening method for Gaussian Process surrogate model assisted evolutionary algorithms

Content Provider	IEEE Xplore Digital Library
Author	Bo Liu Qingfu Zhang Fernandez, F.V. Gielen, G.
Copyright Year	2012
Description	Author affiliation: IMSE, CSIC and University of Sevilla, Spain (Fernandez, F.V.) \|\| ESAT-MICAS, Katholieke Universiteit Leuven, Belgium (Bo Liu; Gielen, G.) \|\| School of Computer Science and Electronic Engineering, University of Essex, Colchester, U.K. (Qingfu Zhang)
Abstract	Surrogate model assisted evolutionary algorithms are receiving much attention for the solution of optimization problems with computationally expensive function evaluations. For small scale problems, the use of a Gaussian Process surrogate model and prescreening methods has proven to be effective. However, each commonly used prescreening method is only suitable for some types of problems, and the proper prescreening method for an unknown problem cannot be stated beforehand. In this paper, the four existing prescreening methods are analyzed and a new method, called self-adaptive lower confidence bound (ALCB), is proposed. The extent of rewarding the prediction uncertainty is adjusted on line based on the density of samples in a local area and the function properties. The exploration and exploitation ability of prescreening can thus be better balanced. Experimental results on benchmark problems show that ALCB has two main advantages: (1) it is more general for different problem landscapes than any of the four existing prescreening methods; (2) it typically can achieve the best result among all available prescreening methods.
Starting Page	1
Ending Page	6
File Size	1158039
Page Count	6
File Format	PDF
ISBN	9781467315104
e-ISBN	9781467315098
e-ISBN	9781467315081
DOI	10.1109/CEC.2012.6256585
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-10
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uncertainty Optimization Evolutionary computation Predictive models Databases Computational modeling Vectors
Content Type	Text
Resource Type	Article

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