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dc.contributor.authorMirjalili, Seyedeh Zahra
dc.contributor.authorSaremi, Shahrzad
dc.contributor.authorMirjalili, Seyed Mohammad
dc.date.accessioned2018-07-19T05:11:33Z
dc.date.available2018-07-19T05:11:33Z
dc.date.issued2015
dc.identifier.issn0941-0643
dc.identifier.doi10.1007/s00521-015-1847-6
dc.identifier.urihttp://hdl.handle.net/10072/101357
dc.description.abstractTraining feedforward neural networks (FNNs) is considered as a challenging task due to the nonlinear nature of this problem and the presence of large number of local solutions. The literature shows that heuristic optimization algorithms are able to tackle these problems much better than the mathematical and deterministic methods. In this paper, we propose a new trainer using the recently proposed heuristic algorithm called social spider optimization (SSO) algorithm. The trained FNN by SSO (FNNSSO) is benchmarked on five standard classification data sets: XOR, balloon, Iris, breast cancer, and heart. The results are verified by the comparison with five other well-known heuristics. The results prove that the proposed FNNSSO is able to provide very promising results compared with other algorithms.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherSpringer
dc.relation.ispartofpagefrom1919
dc.relation.ispartofpageto1928
dc.relation.ispartofissue8
dc.relation.ispartofjournalNeural Computing and Applications
dc.relation.ispartofvolume26
dc.subject.fieldofresearchArtificial intelligence not elsewhere classified
dc.subject.fieldofresearchCognitive and computational psychology
dc.subject.fieldofresearchcode460299
dc.subject.fieldofresearchcode5204
dc.titleDesigning evolutionary feedforward neural networks using social spider optimization algorithm
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.hasfulltextNo Full Text
gro.griffith.authorSaremi, Shahrzad


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