Self-adaptive Lower Confidence Dound: a New General and Effective Prescreening Method for Gaussian Process Surrogate Model Assisted Evolutionary Algorithms

Liu, B. , Zhang, Q., Fernández, F. V. and Gielen, G. (2012) Self-adaptive Lower Confidence Dound: a New General and Effective Prescreening Method for Gaussian Process Surrogate Model Assisted Evolutionary Algorithms. In: 2012 IEEE Congress on Evolutionary Computation, Brisbane, Australia, 10-15 Jun 2012, ISBN 9781467315098 (doi: 10.1109/CEC.2012.6256585)

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.

• Item Type: Conference Proceedings
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Professor Bo
• Authors: Liu, B., Zhang, Q., Fernández, F. V., and Gielen, G.
• College/School: College of Science and Engineering > School of Engineering
• ISSN: 1089-778X
• ISBN: 9781467315098
• Published Online: 02 August 2012