Please use this identifier to cite or link to this item: http://hdl.handle.net/20.500.11889/5315
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dc.contributor.authorMafarja, Majdi
dc.contributor.authorAljarah, Ibrahim
dc.contributor.authorHeidari, Ali Asghar
dc.contributor.authorHammouri, Abdelaziz
dc.contributor.authorFaris, Hossam
dc.contributor.authorAl-Zoubi, Ala
dc.contributor.authorMirjalili, Seyedali
dc.date.accessioned2018-01-09T06:20:22Z
dc.date.available2018-01-09T06:20:22Z
dc.date.issued2017-12-30
dc.identifier.citationMajdi Mafarja, Ibrahim Aljarah, Ali Asghar Heidari, Abdelaziz I. Hammouri, Hossam Faris, Ala’ M. Al-Zoubi, Seyedali Mirjalili, Evolutionary Population Dynamics and Grasshopper Optimization approaches for feature selection problems, Knowledge-Based Systems, Available online 30 December 2017, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2017.12.037.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.11889/5315
dc.description.abstractSearching for the optimal subset of features is known as a challenging problem in feature selection process. To deal with the difficulties involved in this problem, a robust and reliable optimization algorithm is required. In this paper, Grasshopper Optimization Algorithm (GOA) is employed as a search strategy to design a wrapper-based feature selection method. The GOA is a recent population-based metaheuristic that mimics the swarming behaviors of grasshoppers. In this work, an efficient optimizer based on the simultaneous use of the GOA, selection operators, and Evolutionary Population Dynamics (EPD) is proposed in the form of four different strategies to mitigate the immature convergence and stagnation drawbacks of the conventional GOA. In the first two approaches, one of the top three agents and a randomly generated one are selected to reposition a solution from the worst half of the population. In the third and fourth approaches, to give a chance to the low fitness solutions in reforming the population, Roulette Wheel Selection (RWS) and Tournament Selection (TS) are utilized to select the guiding agent from the first half. The proposed GOA_EPD approaches are employed to tackle various feature selection tasks. The proposed approaches are benchmarked on 22 UCI datasets. The comprehensive results and various comparisons reveal that the EPD has a remarkable impact on the efficacy of the GOA and using the selection mechanism enhanced the capability of the proposed approach to outperform other optimizers and find the best solutions with improved convergence trends. Furthermore, the comparative experiments demonstrate the superiority of the proposed approaches when compared to other similar methods in the literature.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectMathematical optimizationen_US
dc.subjectProblem solvingen_US
dc.subjectComputational intelligenceen_US
dc.subjectData miningen_US
dc.subjectEvolutionary computationen_US
dc.subjectGrasshopper optimization algorithm
dc.subject.lcshRough sets
dc.titleEvolutionary population dynamics and grasshopper optimization approaches for feature selection problemsen_US
dc.typeArticleen_US
newfileds.departmentEngineering and Technologyen_US
newfileds.item-access-typeopen_accessen_US
newfileds.thesis-prognoneen_US
newfileds.general-subjectComputers and Information Technology | الحاسوب وتكنولوجيا المعلوماتen_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
item.languageiso639-1other-
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