An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection

dc.creatorChen, Zhiqing
dc.creatorXuan, Ping
dc.creatorAsghar Heidari, Ali
dc.creatorLiu, Lei
dc.creatorWu, Chengwen
dc.creatorChen, Huiling
dc.creatorEscorcia-Gutierrez, José
dc.creatorMansour, Romany F.
dc.date2023-09-18T16:18:48Z
dc.date2023-09-18T16:18:48Z
dc.date2023
dc.date.accessioned2023-10-03T19:09:15Z
dc.date.available2023-10-03T19:09:15Z
dc.identifierZhiqing Chen, Ping Xuan, Ali Asghar Heidari, Lei Liu, Chengwen Wu, Huiling Chen, José Escorcia-Gutierrez, Romany F. Mansour, An artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection, iScience, Volume 26, Issue 5, 2023, 106679, ISSN 2589-0042, https://doi.org/10.1016/j.isci.2023.106679.
dc.identifierhttps://hdl.handle.net/11323/10499
dc.identifier10.1016/j.isci.2023.106679
dc.identifier2589-0042
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9168284
dc.descriptionThe domains of contemporary medicine and biology have generated substantial high-dimensional genetic data. Identifying representative genes and decreasing the dimensionality of the data can be challenging. The goal of gene selection is to minimize computing costs and enhance classification precision. Therefore, this article designs a new wrapper gene selection algorithm named artificial bee bare-bone hunger games search (ABHGS), which is the hunger games search (HGS) integrated with an artificial bee strategy and a Gaussian bare-bone structure to address this issue. To evaluate and validate the performance of our proposed method, ABHGS is compared to HGS and a single strategy embedded in HGS, six classic algorithms, and ten advanced algorithms on the CEC 2017 functions. The experimental results demonstrate that the bABHGS outperforms the original HGS. Compared to peers, it increases classification accuracy and decreases the number of selected features, indicating its actual engineering utility in spatial search and feature selection.
dc.format39 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherElsevier Inc.
dc.publisherUnited States
dc.relationiScience
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dc.rights© 2023 The Author(s).
dc.rightsAtribución 4.0 Internacional (CC BY 4.0)
dc.rightshttps://creativecommons.org/licenses/by/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://www.sciencedirect.com/science/article/pii/S2589004223007563?via%3Dihub
dc.subjectGenetics
dc.subjectComputational bioinformatics
dc.subjectAlgorithms
dc.titleAn artificial bee bare-bone hunger games search for global optimization and high-dimensional feature selection
dc.typeArtículo de revista
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dc.typeText
dc.typeinfo:eu-repo/semantics/article
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