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dc.contributor.authorFaris, Hossam
dc.contributor.authorMirjalili, Seyedali
dc.contributor.authorAljarah, Ibrahim
dc.date.accessioned2020-01-24T05:03:34Z
dc.date.available2020-01-24T05:03:34Z
dc.date.issued2019
dc.identifier.issn1868-8071
dc.identifier.doi10.1007/s13042-018-00913-2
dc.identifier.urihttp://hdl.handle.net/10072/390881
dc.description.abstractIn neural networks, finding optimal values for the number of hidden neurons and connection weights simultaneously is considered a challenging task. This is because altering the hidden neurons substantially impacts the entire structure of a neural network and increases the complexity of training process that requires special considerations. In fact, the number of variables changes proportional to the number of hidden nodes when training neural networks. As one of the seminal attempts, a hybrid encoding scheme is first proposed to deal with the aforementioned challenges. A set of recent and well-regarded stochastic population-based algorithms is then employed to optimize the number of hidden neurons and connection weights in a single hidden feedforward neural network (FFNN). In the experiments, twenty-three standard classification datasets are employed to benchmark the proposed technique qualitatively and quantitatively. The results show that the hybrid encoding scheme allows optimization algorithms to conveniently find the optimal values for both the number of hidden nodes and connection weights. Also, the recently proposed grey wolf optimizer (GWO) outperformed other algorithms.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom2901
dc.relation.ispartofpageto2920
dc.relation.ispartofissue10
dc.relation.ispartofjournalInternational Journal of Machine Learning and Cybernetics
dc.relation.ispartofvolume10
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchcode0801
dc.subject.keywordsScience & Technology
dc.subject.keywordsTechnology
dc.subject.keywordsComputer Science, Artificial Intelligence
dc.subject.keywordsComputer Science
dc.subject.keywordsGrey wolf optimizer
dc.titleAutomatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationFaris, H; Mirjalili, S; Aljarah, I, Automatic selection of hidden neurons and weights in neural networks using grey wolf optimizer based on a hybrid encoding scheme, International Journal of Machine Learning and Cybernetics, 2019, 10 (10), pp. 2901-2920
dc.date.updated2020-01-24T05:00:59Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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