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Dynamic Group Optimisation Algorithm for Training Feed-Forward Neural Networks
Department of Computer and Information Science, University of Macau.
Department of Computer and Information Science, University of Macau.
INNS India Regional Chapter.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-1902-9877
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2018 (English)In: Neurocomputing, ISSN 0925-2312, E-ISSN 1872-8286Article in journal (Refereed) Epub ahead of print
Abstract [en]

Feed-forward neural networks are efficient at solving various types of problems. However, finding efficient training algorithms for feed-forward neural networks is challenging. The dynamic group optimisation (DGO) algorithm is a recently proposed half-swarm half-evolutionary algorithm, which exhibits a rapid convergence rate and good performance in searching and avoiding local optima. In this paper, we propose a new hybrid algorithm, FNNDGO that integrates the DGO algorithm into a feed-forward neural network. DGO plays an optimisation role in training the neural network, by tuning parameters to their optimal values and configuring the structure of feed-forward neural networks. The performance of the proposed algorithm was determined by comparing its performance with those of other training methods in solving two types of problems. The experimental results show that our proposed algorithm exhibits promising performance for solving real-world problems.

Place, publisher, year, edition, pages
Elsevier, 2018.
National Category
Media and Communication Technology
Research subject
Pervasive Mobile Computing
Identifiers
URN: urn:nbn:se:ltu:diva-69627DOI: 10.1016/j.neucom.2018.03.043OAI: oai:DiVA.org:ltu-69627DiVA, id: diva2:1220151
Available from: 2018-06-18 Created: 2018-06-18 Last updated: 2018-06-18

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Vasilakos, Athanasios

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