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dc.contributor.authorVarga, M
dc.contributor.authorGaudl, S
dc.contributor.authorWalker, D
dc.date.accessioned2021-06-16T14:59:31Z
dc.date.issued2021-07
dc.identifier.isbn9781450383516
dc.identifier.urihttp://hdl.handle.net/10026.1/17259
dc.description.abstract

This paper explores the use of geometrical ions (called geons) to represent solutions in the approximated Pareto front generated by multi- and many-objective optimisers. The construction of geons based objects (GBOs)for solutions to a 3- and 5-objective problem is outlined, and the visualisation is embedded in a tool that has been tested with expert users. The findings suggest that our approach is promising, with all users successfully engaging with the given tasks and 4 out of 6 managing to complete some of the tasks they were assigned. Results indicate that the use of geometry, rather than colour as is often used to convey properties of Pareto front approximations, is a useful way of embedding multi-objective data.

dc.format.extent1961-1969
dc.language.isoen
dc.publisherACM
dc.rightsAttribution-ShareAlike 4.0 International
dc.rightsAttribution-ShareAlike 4.0 International
dc.rightsAttribution-ShareAlike 4.0 International
dc.rightsAttribution-ShareAlike 4.0 International
dc.rightsAttribution-ShareAlike 4.0 International
dc.rightsAttribution-ShareAlike 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.rights.urihttp://creativecommons.org/licenses/by-sa/4.0/
dc.subject46 Information and Computing Sciences
dc.subject4602 Artificial Intelligence
dc.titleMany-objective Population Visualisation with Geons
dc.typeconference
dc.typeConference Proceeding
plymouth.date-start2021-07-10
plymouth.date-finish2021-07-14
plymouth.publisher-urlhttps://dl.acm.org/conference/gecco/proceedings
plymouth.conference-nameGECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion
plymouth.publication-statusPublished
plymouth.journalProceedings of the Genetic and Evolutionary Computation Conference Companion
dc.identifier.doi10.1145/3449726.3463162
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/UoA11 Computer Science and Informatics
plymouth.organisational-group/Plymouth/Users by role
plymouth.organisational-group/Plymouth/Users by role/Academics
dcterms.dateAccepted2021-04-12
dc.rights.embargodate2021-8-27
dc.rights.embargoperiodNot known
rioxxterms.versionofrecord10.1145/3449726.3463162
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by-sa/4.0/
rioxxterms.licenseref.startdate2021-07
rioxxterms.typeConference Paper/Proceeding/Abstract


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