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A memetic fuzzy ARTMAP by a grammatical evolution approach
conference contribution
posted on 2016-01-01, 00:00 authored by S C Tan, Chee Peng LimChee Peng Lim, J WatadaThis paper presents a memetic fuzzy ARTMAP (mFAM) model constructed using a grammatical evolution approach. mFAM performs adaptation through a global search with particle swarm optimization (PSO) as well as a local search with the FAM training algorithm. The search and adaptation processes of mFAM are governed by a set of grammatical rules. In the memetic framework, mFAM is constructed and it evolves with a combination of PSO and FAM learning in an arbitrary sequence. A benchmark study is carried out to evaluate and compare the classification performance between mFAM and other state-of-art methods. The results show the effectiveness of mFAM in providing more accurate prediction outcomes.
History
Event
Intelligent Decision Technologies. Conference (8th : 2016 : Puerto de la Cruz, Spain)Volume
56Pagination
447 - 456Publisher
SpringerLocation
Puerto de la Cruz, SpainPlace of publication
Berlin, GermanyPublisher DOI
Start date
2016-06-15End date
2016-06-17ISSN
2190-3018eISSN
2190-3026ISBN-13
9783319396293Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, SpringerEditor/Contributor(s)
I Czarnowski, A Mateos Caballero, R Howlett, L JainTitle of proceedings
KES-IDT 2016 : Proceedings of the 8th KES International Conference on Intelligent Decision TechnolgiesUsage metrics
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