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. Attached Files Name Description MIMEType Size Downloads Title Evolving an adaptive artificial neural network with a gravitational search algorithm Author(s) Tan, Shing Chiang 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: Institute for Intelligent Systems Research and Innovation (IISRI) GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 26 Apr 2016, 12:52:46 EST Tue, 26 Apr 2016, 13:14:13 EST Wed, 27 Apr 2016, 05:01:02 EST Wed, 27 Apr 2016, 06:11:02 EST Thu, 14 Jul 2016, 11:10:45 EST Thu, 14 Jul 2016, 11:18:05 EST Thu, 14 Jul 2016, 11:21:14 EST Thu, 14 Jul 2016, 11:26:21 EST Wed, 03 Aug 2016, 13:46:41 EST Wed, 12 Oct 2016, 15:09:36 EST Wed, 01 Mar 2017, 07:57:15 EST Sat, 22 Jan 2022, 01:31:40 EST Filtered Full Citation counts:   Cited 4 times in TR Web of Science Cited 3 times in Scopus Search Google Scholar Access Statistics: 457 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Tue, 26 Apr 2016, 13:14:13 EST