Show simple item record

dc.contributor.authorWalter, MJ
dc.contributor.authorWalker, DJ
dc.contributor.authorCraven, MJ
dc.date.accessioned2022-04-13T08:09:59Z
dc.date.issued2022-03-07
dc.identifier.issn1089-778X
dc.identifier.issn1941-0026
dc.identifier.other6
dc.identifier.urihttp://hdl.handle.net/10026.1/19030
dc.description.abstract

This work assesses the efficacy of evolutionary algorithms (EAs) using an intuitive Multi-Dimensional Scaling (MDS) visualisation of the evolution of a population. We propose the use of Landmark Multi-Dimensional Scaling (LMDS) to overcome computational challenges inherent to visualising many-objective and complex problems with MDS. For the benchmark problems we tested, LMDS is akin to MDS visually, whilst requiring less than 1% of the time and memory necessary to produce an MDS visualisation of the same objective space solutions, leading to the possibility of online visualisations for multiand many-objective optimisation evaluation. Using multi- and many-objective problems from the DTLZ and WFG benchmark test suites, we analyse how Landmark MDS visualisations can offer far greater insight into algorithm performance than using traditional algorithm performance metrics such as hypervolume alone, and can be used to complement explicit performance metrics. Ultimately, this visualisation allows visual identification of problem features and assists the decision maker in making intuitive recommendations for algorithm parameters/operators for creating and testing better EAs to solve multi- and manyobjective problems.

dc.format.extent1501-1510
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.subjectLandmark multi-dimensional scaling
dc.subjectMany-objective optimisation
dc.subjectMulti-objective optimisation
dc.subjectVisualisation
dc.titleVisualising Population Dynamics to Examine Algorithm Performance
dc.typejournal-article
dc.typeArticle
plymouth.author-urlhttps://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000892933300024&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008
plymouth.issue6
plymouth.volume26
plymouth.publisher-urlhttp://dx.doi.org/10.1109/tevc.2022.3157143
plymouth.publication-statusPublished
plymouth.journalIEEE Transactions on Evolutionary Computation
dc.identifier.doi10.1109/TEVC.2022.3157143
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.dateAccepted2022-02-10
dc.rights.embargodate2022-4-22
dc.identifier.eissn1941-0026
rioxxterms.versionAccepted Manuscript
rioxxterms.versionofrecord10.1109/TEVC.2022.3157143
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2022-03-31
rioxxterms.typeJournal Article/Review


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record


All items in PEARL are protected by copyright law.
Author manuscripts deposited to comply with open access mandates are made available in accordance with publisher policies. Please cite only the published version using the details provided on the item record or document. In the absence of an open licence (e.g. Creative Commons), permissions for further reuse of content should be sought from the publisher or author.
Theme by 
Atmire NV