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dc.contributor.authorMafarja, M
dc.contributor.authorHeidari, AA
dc.contributor.authorFaris, H
dc.contributor.authorMirjalili, S
dc.contributor.authorAljarah, I
dc.contributor.editorMirjalili, Seyedali
dc.contributor.editorDong, Jin Song
dc.contributor.editorLewis, Andrew
dc.description.abstractIn this chapter, a wrapper-based feature selection algorithm is designed and substantiated based on the binary variant of Dragonfly Algorithm (BDA). DA is a successful, well-established metaheuristic that revealed superior efficacy in dealing with various optimization problems including feature selection. In this chapter we are going first present the inspirations and methamatical modeds of DA in details. Then, the performance of this algorithm is tested on a special type of datasets that contain a huge number of features with low number of samples. This type of datasets makes the optimization process harder, because of the large search space, and the lack of adequate samples to train the model. The experimental results showed the ability of DA to deal with this type of datasets better than other optimizers in the literature. Moreover, an extensive literature review for the DA is provided in this chapter.
dc.relation.ispartofbooktitleNature-Inspired Optimizers: Theories, Literature Reviews and Applications
dc.relation.ispartofseriesStudies in Computational Intelligence
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.titleDragonfly algorithm: Theory, literature review, and application in feature selection
dc.typeBook chapter
dc.type.descriptionB1 - Chapters
dcterms.bibliographicCitationMafarja, M; Heidari, AA; Faris, H; Mirjalili, S; Aljarah, I, Dragonfly algorithm: Theory, literature review, and application in feature selection, Nature-Inspired Optimizers, 2020, pp. 47-67
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali

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