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Fuzzy clustering based Gaussian Process Model for large training set and its application in expensive evolutionary optimization

Liu, Wudong and Zhang, Qingfu and Tsang, Edward and Virginas, Botond (2009) Fuzzy clustering based Gaussian Process Model for large training set and its application in expensive evolutionary optimization. In: 2009 IEEE Congress on Evolutionary Computation (CEC), 2009-05-18 - 2009-05-21.

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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
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 15 Aug 2012 13:16
Last Modified: 15 Jan 2022 00:39
URI: http://repository.essex.ac.uk/id/eprint/1987

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