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A Type Detection Based Dynamic Multi-objective Evolutionary Algorithm

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dc.contributor.authorTOPCUOĞLU, HALUK RAHMİ
dc.contributor.authorsSahmoud, Shaaban; Topcuoglu, Haluk Rahmi
dc.contributor.editorSim, K
dc.contributor.editorKaufmann, P
dc.contributor.editorAscheid, G
dc.contributor.editorBacardit, J
dc.contributor.editorCagnoni, S
dc.contributor.editorCotta, C
dc.contributor.editorDAndreagiovanni, F
dc.contributor.editorDivina, F
dc.contributor.editorEsparciaAlcazar, AL
dc.contributor.editorDeVega, FF
dc.contributor.editorGlette, K
dc.contributor.editorHidalgo, JI
dc.contributor.editorHubert, J
dc.contributor.editorIacca, G
dc.contributor.editorKramer, O
dc.contributor.editorMavrovouniotis, M
dc.contributor.editorGarcia, AMM
dc.contributor.editorNguyen, TT
dc.contributor.editorSchaefer, R
dc.contributor.editorSilva, S
dc.contributor.editorTonda, A
dc.contributor.editorUrquhart, N
dc.contributor.editorZhang, M
dc.date.accessioned2022-03-12T16:23:46Z
dc.date.available2022-03-12T16:23:46Z
dc.date.issued2018
dc.description.abstractCharacterization of dynamism is an important issue for utilizing or tailoring of several dynamic multi-objective evolutionary algorithms (DMOEAs). One such characterization is the change detection, which is based on proposing explicit schemes to detect the points in time when a change occurs. Additionally, detecting severity of change and incorporating with the DMOEAs is another attempt of characterization, where there is only a few related works presented in the literature. In this paper, we propose a type-detection mechanism for dynamic multi-objective optimization problems, which is one of the first attempts that investigate the significance of type detection on the performance of DMOEAs. Additionally, a hybrid technique is proposed which incorporates our type detection mechanism with a given DOMEA. We present an empirical evaluation by using seven test problems from all four types and five performance metrics, which clearly validate the motivation of type detection as well as significance of our hybrid technique.
dc.identifier.doi10.1007/978-3-319-77538-8_58
dc.identifier.eissn1611-3349
dc.identifier.isbn978-3-319-77538-8
dc.identifier.issn0302-9743
dc.identifier.urihttps://hdl.handle.net/11424/226045
dc.identifier.wosWOS:000433244800058
dc.language.isoeng
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AG
dc.relation.ispartofAPPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2018
dc.relation.ispartofseriesLecture Notes in Computer Science
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectDynamic Multi-objective Optimization Problems
dc.subjectNon-dominated Sorting Genetic Algorithm (NSGA-II)
dc.subjectType detection
dc.subjectDynamic Multi-objective Evolutionary Algorithms
dc.subjectNSGA-II
dc.subjectOPTIMIZATION
dc.titleA Type Detection Based Dynamic Multi-objective Evolutionary Algorithm
dc.typeconferenceObject
dspace.entity.typePublication
local.avesis.id36c68c25-a24b-4316-b572-1b05bd8f6567
local.conference.dateAPR 04-06, 2018
local.conference.locationParma, ITALY
local.conference.title21st International Conference on the Applications of Evolutionary Computation (EvoApplications)
local.import.packageSS15
local.indexed.atWOS
local.indexed.atSCOPUS
local.journal.numberofpages15
oaire.citation.endPage893
oaire.citation.startPage879
oaire.citation.titleAPPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2018
oaire.citation.volume10784
relation.isAuthorOfPublication54c6a927-2146-44b3-90ee-33dac6503317
relation.isAuthorOfPublication.latestForDiscovery54c6a927-2146-44b3-90ee-33dac6503317

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