Evolving an adaptive artificial neural network with a gravitational search algorithm

Tan, Shing Chiang and Lim, Chee Peng 2015, Evolving an adaptive artificial neural network with a gravitational search algorithm, in KES-IDT 2015 : Proceedings of the 7th International Conference on Intelligent Decision Technologies, Springer, Cham, Switzerland, pp. 599-609, doi: 10.1007/978-3-319-19857-6_51.

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Title Evolving an adaptive artificial neural network with a gravitational search algorithm
Author(s) Tan, Shing Chiang
Lim, Chee PengORCID iD for Lim, Chee Peng orcid.org/0000-0003-4191-9083
Conference name KES Intelligent Decision Technologies. International Conference (7th : 2015 : Sorrento, Italy)
Conference location Sorrento, Italy
Conference dates 17-19 Jul. 2015
Title of proceedings KES-IDT 2015 : Proceedings of the 7th International Conference on Intelligent Decision Technologies
Publication date 2015
Series Smart Innovation, Systems and Technologies, v.39
Conference series Intelligent Decision Technologies
Start page 599
End page 609
Total pages 11
Publisher Springer
Place of publication Cham, Switzerland
Summary In this paper, a supervised fuzzy adaptive resonance theory neural network, i.e., Fuzzy ARTMAP (FAM), is integrated with a heuristic Gravitational Search Algorithm (GSA) that is inspired from the laws of Newtonian gravity. The proposed FAM-GSA model combines the unique features of both constituents to perform data classification. The classification performance of FAM-GSA is benchmarked against other state-of-art machine learning classifiers using an artificially generated data set and two real data sets from different domains. Comparatively, the empirical results indicate that FAM-GSA generally is able to achieve a better classification performance with a parsimonious network size, but with the expense of a higher computational load.
ISBN 9783319198569
ISSN 2190-3018
2190-3026
Language eng
DOI 10.1007/978-3-319-19857-6_51
Field of Research 080108 Neural, Evolutionary and Fuzzy Computation
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083049

Document type: Conference Paper
Collections: Centre for Intelligent Systems Research
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