Improving the Performance of Fuzzy ARTMAP with Hybrid Evolutionary Programming: An Experimental Study

Authors:
Shing Chiang Tan;Chee Peng Lim
Affiliations:
Faculty of Information Science & Technology, Multimedia University, Melaka, Malaysia 75450;School of Electrical & Electronic Engineering, University of Science Malaysia, Nibong Tebal, Malaysia 14300
Venue:
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Year:
2009

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Abstract

This paper presents an evolutionary artificial neural network (EANN) that combines the operations of Fuzzy ARTMAP (FAM) and a Hybrid Evolutionary Programming (HEP) model in a sequential manner. The proposed FAM-HEP network, which harnesses the advantages of FAM and HEP, is able to construct its network architecture autonomously, and to perform learning and evolutionary search and adaptation. In order to evaluate the effectiveness of the proposed FAM-HEP network, an experimental study using benchmark data sets is conducted. The performance of FAM-HEP is analyzed, and the results are compared with those of FAM-EP and FAM. Overall, FAM-HEP outperforms FAM-EP and FAM. The study also reveals the potential of FAM-HEP as an innovative EANN model for undertaking pattern classification problems.