Visualisation with Treemaps and Sunbursts in Evolutionary Many-objective Optimisation
dc.contributor.author | Walker, DJ | |
dc.date.accessioned | 2018-09-28T13:50:04Z | |
dc.date.issued | 2018-09 | |
dc.identifier.issn | 1389-2576 | |
dc.identifier.issn | 1573-7632 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/12429 | |
dc.description.abstract |
Visualisation is an important aspect of evolutionary computation, enabling practitioners to explore the operation of their algorithms in an intuitive way and providing a better means for displaying their results to problem owners. The presentation of the complex data arising in many-objective evolutionary algorithms remains a challenge, and this work examines the use of treemaps and sunbursts for visualising such data. We present a novel algorithm for arranging a treemap so that it explicitly displays the dominance relations that characterise many-objective populations, as well as considering approaches for creating trees with which to represent multi- and many-objective solutions. We show that treemaps and sunbursts can be used to display important aspects of evolutionary computation, such as the diversity and convergence of a search population, and demonstrate the approaches on a range of test problems and a real-world problem from the literature. | |
dc.format.extent | 421-452 | |
dc.format.medium | Print-Electronic | |
dc.language | en | |
dc.language.iso | en | |
dc.publisher | Springer Verlag | |
dc.subject | Many-objective optimisation | |
dc.subject | Visualisation | |
dc.subject | Evolutionary computation | |
dc.title | Visualisation with Treemaps and Sunbursts in Evolutionary Many-objective Optimisation | |
dc.type | journal-article | |
dc.type | Journal Article | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000441941500005&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.issue | 3 | |
plymouth.volume | 19 | |
plymouth.publication-status | Published | |
plymouth.journal | Genetic Programming and Evolvable Machines | |
dc.identifier.doi | 10.1007/s10710-018-9329-0 | |
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 | |
dc.publisher.place | United States | |
dcterms.dateAccepted | 2018-07-11 | |
dc.rights.embargodate | 2019-12-17 | |
dc.identifier.eissn | 1573-7632 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.versionofrecord | 10.1007/s10710-018-9329-0 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2018-09 | |
rioxxterms.type | Journal Article/Review |