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dc.contributor.authorBouaziz, Souhir
dc.contributor.authorDhahri, Habib
dc.contributor.authorAlimi, Adel M.
dc.contributor.authorAbraham, Ajith
dc.date.accessioned2016-10-10T07:48:59Z
dc.date.available2016-10-10T07:48:59Z
dc.date.issued2016
dc.identifier.citationApplied Soft Computing. 2016, vol. 47, p. 653-668.cs
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.urihttp://hdl.handle.net/10084/112139
dc.description.abstracttIn this paper, a new hybrid learning algorithm is introduced to evolve the flexible beta basis functionneural tree (FBBFNT). The structure is developed using the Extended Genetic Programming (EGP) and theBeta parameters and connected weights are optimized by the Hybrid Artificial Bee Colony algorithm. Thishybridization is essentially based on replacing the random Artificial Bee Colony (ABC) position with theguided Opposite-based Particle Swarm Optimization (OPSO) position. Such modification can minimizethe delay which might be lead by the random position, in reaching the global solution. The performanceof the proposed model is evaluated for benchmark problems drawn from time series prediction area andis compared with those of related methods.cs
dc.language.isoencs
dc.publisherElseviercs
dc.relation.ispartofseriesApplied Soft Computingcs
dc.relation.urihttp://dx.doi.org/10.1016/j.asoc.2016.03.006cs
dc.rights© 2016 Elsevier B.V. All rights reserved.cs
dc.subjectFlexible beta basis function neural tree modelcs
dc.subjectExtended Genetic Programmingcs
dc.subjectHybrid Artificial Bee Colony algorithmcs
dc.subjecttime-series forecastingcs
dc.titleEvolving flexible beta basis function neural tree using extended genetic programming & Hybrid Artificial Bee Colonycs
dc.typearticlecs
dc.identifier.doi10.1016/j.asoc.2016.03.006
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume47cs
dc.description.lastpage668cs
dc.description.firstpage653cs
dc.identifier.wos000380935400048


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