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Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm
Joint Authors
Zhou, Yongquan
Wu, Haizhou
Luo, Qifang
Basset, Mohamed Abdel
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2016, Issue 2016 (31 Dec. 2015), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-12-25
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Symbiotic organisms search (SOS) is a new robust and powerful metaheuristic algorithm, which stimulates the symbiotic interaction strategies adopted by organisms to survive and propagate in the ecosystem.
In the supervised learning area, it is a challenging task to present a satisfactory and efficient training algorithm for feedforward neural networks (FNNs).
In this paper, SOS is employed as a new method for training FNNs.
To investigate the performance of the aforementioned method, eight different datasets selected from the UCI machine learning repository are employed for experiment and the results are compared among seven metaheuristic algorithms.
The results show that SOS performs better than other algorithms for training FNNs in terms of converging speed.
It is also proven that an FNN trained by the method of SOS has better accuracy than most algorithms compared.
American Psychological Association (APA)
Wu, Haizhou& Zhou, Yongquan& Luo, Qifang& Basset, Mohamed Abdel. 2016. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm. Computational Intelligence and Neuroscience،Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099813
Modern Language Association (MLA)
Wu, Haizhou…[et al.]. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm. Computational Intelligence and Neuroscience Vol. 2016, no. 2016 (2015), pp.1-14.
https://search.emarefa.net/detail/BIM-1099813
American Medical Association (AMA)
Wu, Haizhou& Zhou, Yongquan& Luo, Qifang& Basset, Mohamed Abdel. Training Feedforward Neural Networks Using Symbiotic Organisms Search Algorithm. Computational Intelligence and Neuroscience. 2016. Vol. 2016, no. 2016, pp.1-14.
https://search.emarefa.net/detail/BIM-1099813
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1099813