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dc.contributor.authorSanchez-Anguix, V.
dc.contributor.authorChalumuri, R.
dc.contributor.authorAydoğan, Reyhan
dc.contributor.authorJulian, V.
dc.date.accessioned2020-09-08T06:25:24Z
dc.date.available2020-09-08T06:25:24Z
dc.date.issued2019-03
dc.identifier.issn1568-4946en_US
dc.identifier.urihttp://hdl.handle.net/10679/6916
dc.identifier.urihttps://www.sciencedirect.com/science/article/abs/pii/S1568494618306811
dc.description.abstractThe problem of allocating students to supervisors for the development of a personal project or a dissertation is a crucial activity in the higher education environment, as it enables students to get feedback on their work from an expert and improve their personal, academic, and professional abilities. In this article, we propose a multi-objective and near Pareto optimal genetic algorithm for the allocation of students to supervisors. The allocation takes into consideration the students and supervisors' preferences on research/project topics, the lower and upper supervision quotas of supervisors, as well as the workload balance amongst supervisors. We introduce novel mutation and crossover operators for the studentsupervisor allocation problem. The experiments carried out show that the components of the genetic algorithm are more apt for the problem than classic components, and that the genetic algorithm is capable of producing allocations that are near Pareto optimal in a reasonable time.en_US
dc.description.sponsorshipFaculty of Engineering and Computing at Coventry University, United Kingdom ; European Commission Joint Research Centre
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computing
dc.rightsrestrictedAccess
dc.titleA near Pareto optimal approach to student–supervisor allocation with two sided preferences and workload balanceen_US
dc.typeArticleen_US
dc.peerreviewedyesen_US
dc.publicationstatusPublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID 0000-0002-5260-9999 & YÖK ID 145578) Aydoğan, Reyhan
dc.contributor.ozuauthorAydoğan, Reyhan
dc.identifier.volume76en_US
dc.identifier.startpage1en_US
dc.identifier.endpage15en_US
dc.identifier.wosWOS:000461145200001
dc.identifier.doi10.1016/j.asoc.2018.11.049en_US
dc.subject.keywordsGenetic algorithmsen_US
dc.subject.keywordsStudent-project allocationen_US
dc.subject.keywordsMatchingen_US
dc.subject.keywordsPareto optimalen_US
dc.subject.keywordsArtificial intelligenceen_US
dc.identifier.scopusSCOPUS:2-s2.0-85058435062
dc.contributor.authorFemale1
dc.relation.publicationcategoryArticle - International Refereed Journal - Institution Academic Staff


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