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Metaheuristic Optimization Algorithms for Training Artificial Neural Networks

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dc.contributor.author Mansour, Nashat
dc.contributor.author Al Kawam, Ahmad
dc.date.accessioned 2016-01-26T09:57:05Z
dc.date.available 2016-01-26T09:57:05Z
dc.date.copyright 2012
dc.date.issued 2016-01-26
dc.identifier.issn 2279 – 0764 en_US
dc.identifier.uri http://hdl.handle.net/10725/2956
dc.description.abstract Training neural networks is a complex task that is important for supervised learning. A few metaheuristic optimization techniques have been applied to increase the effectiveness of the training process. The Cuckoo Search (CS) algorithm is a recently developed meta-heuristic optimization algorithm which is suitable for solving optimization problems. In this paper, Cuckoo search is implemented in training a feed forward multilayer Perceptron network (MLP). We then evaluate the trained MLP‟s accuracy by applying four benchmark classification problems. Furthermore, the results obtained are compared to those attained using another competing meta-heuristic which is the Particle Swarm Optimization (PSO). Also, Guaranteed Convergence Particle Swarm Optimization (GCPSO) which is a PSO variant is implemented and its results are compared with CS and PSO. CS proved to be superior to PSO and GCPSO in all benchmark problems. en_US
dc.language.iso en en_US
dc.title Metaheuristic Optimization Algorithms for Training Artificial Neural Networks en_US
dc.type Article en_US
dc.description.version Published en_US
dc.author.school SAS en_US
dc.author.idnumber 198629170 en_US
dc.author.woa N/A en_US
dc.author.department Computer Science and Mathematics en_US
dc.description.embargo N/A en_US
dc.relation.journal International Journal of Computer and Information Technology en_US
dc.journal.volume 1 en_US
dc.journal.issue 2 en_US
dc.article.pages 156-161 en_US
dc.keywords Metaheuristic Algorithms en_US
dc.keywords ANN Training en_US
dc.keywords MLP en_US
dc.keywords Cuckoo Search en_US
dc.keywords Particle Swarm Optimization en_US
dc.identifier.ctation Kawam, A. A., & Mansour, N. (2012). Metaheuristic optimization algorithms for training artificial neural networks. Int. J. Comput. Inf. Technol, 1, 156-161. en_US
dc.author.email nmansour@lau.edu.lb
dc.identifier.url http://ijcit.com/archives/volume1/issue2/Paper010221.pdf


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