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

Liu, W and Zhang, Q and Tsang, E and Virginas, B (2009) Fuzzy clustering based gaussian process model for large training set and its application in expensive evolutionary optimization. In: UNSPECIFIED, ? - ?.

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Abstract

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 optimizationproblems. © 2009 IEEE.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: 2009 IEEE Congress on Evolutionary Computation, CEC 2009
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Jim Jamieson
Date Deposited: 15 Aug 2012 13:16
Last Modified: 15 Nov 2018 18:27
URI: http://repository.essex.ac.uk/id/eprint/1987

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