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dc.contributor.authorOsaba, Eneko
dc.contributor.authorDel Ser, Javier
dc.contributor.authorMartinez, Aritz D.
dc.contributor.authorHussain, Amir
dc.date.accessioned2022-12-14T17:17:03Z
dc.date.available2022-12-14T17:17:03Z
dc.date.issued2022-04-12
dc.identifier.citationOsaba, E., Del Ser, J., Martinez, A.D. et al. Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions. Cogn Comput 14, 927–954 (2022). https://doi.org/10.1007/s12559-022-10012-8en
dc.identifier.issn1866-9956en
dc.identifier.urihttp://hdl.handle.net/11556/1451
dc.description.abstractIn this work, we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of evolutionary multitasking tackles multitask optimization scenarios by using biologically inspired concepts drawn from swarm intelligence and evolutionary computation. The main purpose of this survey is to collect, organize, and critically examine the abundant literature published so far in evolutionary multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can leverage the potential of biologically inspired algorithms for multitask optimization. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.en
dc.description.sponsorshipThe authors would like to thank the Basque Government for its funding support through the ELKARTEK program (3KIA project, KK-2020/00049) and the consolidated research group MATHMODE (ref. T1294-19).en
dc.language.isoengen
dc.publisherSpringeren
dc.titleEvolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directionsen
dc.typearticleen
dc.identifier.doi10.1007/s12559-022-10012-8en
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsTransfer optimizationen
dc.subject.keywordsMultitasking optimizationen
dc.subject.keywordsEvolutionary multitaskingen
dc.subject.keywordsMultifactorial evolutionary algorithmen
dc.subject.keywordsMulti-population multitaskingen
dc.identifier.essn1866-9964en
dc.issue.number3en
dc.journal.titleCognitive Computationen
dc.page.final954en
dc.page.initial927en
dc.volume.number14en


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