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The Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancement

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dc.citedby5
dc.contributor.authorHosseini E.en_US
dc.contributor.authorAl-Ghaili A.M.en_US
dc.contributor.authorKadir D.H.en_US
dc.contributor.authorDaneshfar F.en_US
dc.contributor.authorGunasekaran S.S.en_US
dc.contributor.authorDeveci M.en_US
dc.contributor.authorid57212521533en_US
dc.contributor.authorid26664381500en_US
dc.contributor.authorid57211243421en_US
dc.contributor.authorid35078447100en_US
dc.contributor.authorid55652730500en_US
dc.contributor.authorid55734383000en_US
dc.date.accessioned2025-03-03T07:46:35Z
dc.date.available2025-03-03T07:46:35Z
dc.date.issued2024
dc.description.abstractIn the past few decades, there have been multiple algorithms proposed for the purpose of solving optimization problems including Machine Learning (ML) applications. Among these algorithms, metaheuristics are an appropriate tool to solve these real problems. Also, ML is one of the advanced tools in Artificial Intelligence (AI) including different learning strategies to teach new tasks according to data. Therefore, proposing an efficient meta-heuristic to improve the inputs of the trainer in ML would be significant. In this study, a new idea centered on seed growth, Seed Growth Algorithm (SGA), as a conditional convergent evolutionary algorithm is proposed for optimizing several discrete and continuous optimization problems. SGA is used in the process of solving optimization test problems by neural networks. The problems are solved by the same neural network with and without SGA, computational results prove the efficiency of SGA in neural networks. Finally, SGA is proposed to solve very extensive test problems including IoT optimization problems. Comparative results of applying the SGA on all of these problems with different sizes are included, and the proposed algorithm suggests effective solutions within a reasonable timeframe. ? 2013 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1109/ACCESS.2024.3452511
dc.identifier.epage127459
dc.identifier.scopus2-s2.0-85203538553
dc.identifier.spage127440
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85203538553&doi=10.1109%2fACCESS.2024.3452511&partnerID=40&md5=f16431ec98a29f60821e76fade764ec3
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37009
dc.identifier.volume12
dc.pagecount19
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofAll Open Access; Gold Open Access
dc.sourceScopus
dc.sourcetitleIEEE Access
dc.subjectAdversarial machine learning
dc.subjectConvergent algorithms
dc.subjectGrowth algorithms
dc.subjectMachine-learning
dc.subjectMeta-heuristic approach
dc.subjectMetaheuristic
dc.subjectMultiple algorithms
dc.subjectNeural-networks
dc.subjectOptimization problems
dc.subjectSeed growth algorithm
dc.subjectSeed growths
dc.subjectOptimization algorithms
dc.titleThe Evolutionary Convergent Algorithm: A Guiding Path of Neural Network Advancementen_US
dc.typeArticleen_US
dspace.entity.typePublication
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