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dc.contributor.authorWalker, D
dc.contributor.authorCraven, M
dc.date.accessioned2019-11-25T15:17:32Z
dc.date.issued2019-11-28
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.other105902
dc.identifier.urihttp://hdl.handle.net/10026.1/15177
dc.description.abstract

Evolutionary algorithms are often highly dependent on the correct setting of their parameters, and benchmarking different parametrisations allows a user to identify which parameters offer the best performance on their given problem. Visualisation offers a way of presenting the results of such benchmarking so that a non-expert user can understand how their algorithm is performing. By examining the characteristics of their algorithm, such as convergence and diversity, the user can learn how effective their chosen algorithm parametrisation is. This paper presents a technique intended to offer this insight, by presenting the relative performance of a set of EAs optimising the same multi-objective problem in a simple visualisation. The visualisation characterises the behaviour of the algorithm in terms of known performance indicators drawn from the literature, and is capable of visualising the optimisation of many-objective problems also. The method is demonstrated with benchmark test problems from the popular DTLZ and CEC 2009 problem suites, optimising them with different parametrisations of both NSGA-II and NSGA-III, and it is shown that known characteristics of optimisers solving these problems can be observed in the visualisations resulting.

dc.format.extent0-0
dc.languageen
dc.language.isoen
dc.publisherElsevier
dc.subjectBenchmarking
dc.subjectParametrisation
dc.subjectVisualisation
dc.subjectMany-Objective
dc.subjectMulti-Objective
dc.subjectOptimisation
dc.titleIdentifying Good Algorithm Parameters in Evolutionary Multi- and Many-Objective Optimisation: A Visualisation Approach
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000515094200035&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.volume88
plymouth.publisher-urlhttp://dx.doi.org/10.1016/j.asoc.2019.105902
plymouth.publication-statusPublished
plymouth.journalApplied Soft Computing
dc.identifier.doi10.1016/j.asoc.2019.105902
plymouth.organisational-group/Plymouth
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering
plymouth.organisational-group/Plymouth/Faculty of Science and Engineering/School of Engineering, Computing and Mathematics
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/EXTENDED UoA 10 - Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA10 Mathematical Sciences
plymouth.organisational-group/Plymouth/REF 2021 Researchers by UoA/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2019-10-29
dc.rights.embargodate2020-11-27
dc.identifier.eissn1872-9681
dc.rights.embargoperiodNot known
rioxxterms.versionAccepted Manuscript
rioxxterms.versionofrecord10.1016/j.asoc.2019.105902
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2019-11-28
rioxxterms.typeJournal Article/Review


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