Fuzzy clustering based Gaussian process model for large training set and its application in expensive evolutionary optimization

Authors:
Wudong Liu;Qingfu Zhang;Edward Tsang;Botond Virginas
Affiliations:
School of Computer Science & Electronic Engineering, University of Essex, Colchester, UK;School of Computer Science & Electronic Engineering, University of Essex, Colchester, UK;School of Computer Science & Electronic Engineering, University of Essex, Colchester, UK;BT, Ipswich, UK
Venue:
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Year:
2009

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Abstract

Gaussian process model is an effective and efficient method for approximating a continuous function. However, its computational cost increases exponentially with the size of training data set. A very popular way to alleviate this shortcoming is to cluster the whole training data set into a number of small clusters and then a local model is built for each cluster. However, widely used crisp clustering might not be accurate in the boundary areas among different clusters. This paper proposes a fuzzy clustering based method for improving approximation quality. Several clusters with overlaps are firstly obtained by Fuzzy C-Means clustering and then local models are built for these clusters. It has been demonstrated that this method can be used with evolutionary algorithms for dealing expensive optimization problems.