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Evolving an adaptive artificial neural network with a gravitational search algorithm
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.
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Event
KES Intelligent Decision Technologies. International Conference (7th : 2015 : Sorrento, Italy)Volume
39Series
Smart Innovation, Systems and TechnologiesPagination
599 - 609Publisher
SpringerLocation
Sorrento, ItalyPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2015-07-17End date
2015-07-19ISSN
2190-3018eISSN
2190-3026ISBN-13
9783319198569Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, SpringerTitle of proceedings
KES-IDT 2015 : Proceedings of the 7th International Conference on Intelligent Decision TechnologiesUsage metrics
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