Algorithm selection on adaptive operator selection : a case study on genetic algorithms

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dc.authoridMustafa Mısır / 0000-0002-6885-6775en_US
dc.authorscopusidMustafa Mısır / 36458858100en_US
dc.authorwosidMustafa Mısır / A-6739-2010
dc.contributor.authorMısır, Mustafa
dc.date.accessioned2022-01-19T10:41:40Z
dc.date.available2022-01-19T10:41:40Z
dc.date.issued2021en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractThe present study applies Algorithm Selection (AS) to Adaptive Operator Selection (AOS) for further improving the performance of the AOS methods. AOS aims at delivering high performance in solving a given problem through combining the strengths of multiple operators. Although the AOS methods are expected to outperform running each operator separately, there is no one AOS method can consistently perform the best. Thus, there is still room for improvement which can be provided by using the best AOS method for each problem instance being solved. For this purpose, the AS problem on AOS is investigated. The underlying AOS methods are applied to choose the crossover operator for a Genetic Algorithm (GA). The Quadratic Assignment Problem (QAP) is used as the target problem domain. For carrying out AS, a suite of simple and easy-to-calculate features characterizing the QAP instances is introduced. The corresponding empirical analysis revealed that AS offers improved performance and robustness by utilizing the strenghts of different AOS approaches. © 2021, Springer Nature Switzerland AG.en_US
dc.identifier.citationMısır, M. (2021). Algorithm selection on Adaptive operator selection: A Case study on Genetic algorithms doi:10.1007/978-3-030-92121-7_20en_US
dc.identifier.doi10.1007/978-3-030-92121-7_20en_US
dc.identifier.endpage251en_US
dc.identifier.isbn9783030921200
dc.identifier.scopus2-s2.0-85121924066en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage237en_US
dc.identifier.urihttps://doi.org/10.1007/978-3-030-92121-7_20
dc.identifier.urihttps://hdl.handle.net/20.500.12713/2403
dc.identifier.volume12931en_US
dc.identifier.wosWOS:000922798500020en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorMısır, Mustafa
dc.language.isoenen_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofLecture notes in computer science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.relation.tubitakinfo:eu-repo/grantAgreement/TUBITAK/SOBAG/119C013
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectGenetic Algorithmsen_US
dc.subjectCombinatorial Optimizationen_US
dc.titleAlgorithm selection on adaptive operator selection : a case study on genetic algorithmsen_US
dc.typeConference Objecten_US

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