Evolutionary Multitask Optimization: Fundamental research questions, practices, and directions for the future

Simple item page
dc.contributor.authorOsaba, Eneko
dc.contributor.authorDel Ser, Javier
dc.contributor.authorSuganthan, Ponnuthurai N.
dc.contributor.institutionQuantum
dc.contributor.institutionIA
dc.date.issued2022-12
dc.descriptionPublisher Copyright: © 2022 Elsevier B.V.
dc.description.abstractTransfer Optimization has gained a remarkable attention from the Swarm and Evolutionary Computation community in the recent years. It is undeniable that the concepts underlying Transfer Optimization are formulated on solid grounds. However, evidences observed in recent contributions confirm that there are critical aspects that are not properly addressed to date. This short communication aims to engage the readership around a reflection on these issues, and to provide rationale why they remain unsolved. Specifically, we emphasize on three critical points of Evolutionary Multitasking Optimization: (i) the plausibility and practical applicability of this paradigm; (ii) the novelty of some proposed multitasking methods; and (iii) the methodologies used for evaluating newly proposed multitasking algorithms. As a result of this research, we conclude that some important efforts should be directed by the community in order to keep the future of this promising field on the right track. Our ultimate purpose is to unveil gaps in the current literature, so that prospective works can attempt to fix these gaps, avoiding to stumble on the same stones and eventually achieve valuable advances in the area.en
dc.description.sponsorshipThe authors would like to thank the Basque Government, Spain for its funding support through the ELKARTEK program and the consolidated research group MATHMODE (ref. IT1456-22 ).
dc.description.statusPeer reviewed
dc.identifier.citationOsaba , E , Del Ser , J & Suganthan , P N 2022 , ' Evolutionary Multitask Optimization : Fundamental research questions, practices, and directions for the future ' , Swarm and Evolutionary Computation , vol. 75 , 101203 . https://doi.org/10.1016/j.swevo.2022.101203
dc.identifier.doi10.1016/j.swevo.2022.101203
dc.identifier.issn2210-6502
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85141922374&partnerID=8YFLogxK
dc.language.isoeng
dc.relation.ispartofSwarm and Evolutionary Computation
dc.relation.projectIDEusko Jaurlaritza, IT1456-22
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject.keywordsEvolutionary multitasking
dc.subject.keywordsMultifactorial evolutionary algorithm
dc.subject.keywordsMultitasking optimization
dc.subject.keywordsTransfer Optimization
dc.subject.keywordsGeneral Computer Science
dc.subject.keywordsGeneral Mathematics
dc.titleEvolutionary Multitask Optimization: Fundamental research questions, practices, and directions for the futureen
dc.typejournal article
Files