Toward the Online Visualisation of Algorithm Performance for Parameter Selection
dc.contributor.author | Walker, David | |
dc.contributor.author | Craven, Matthew | |
dc.contributor.editor | Sim K | |
dc.contributor.editor | Kaufmann P | |
dc.date.accessioned | 2018-02-22T16:28:24Z | |
dc.date.available | 2018-02-22T16:28:24Z | |
dc.date.issued | 2018 | |
dc.identifier.isbn | 9783319775371 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.issn | 1611-3349 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/10846 | |
dc.description.abstract |
A visualisation method is presented that is intended to assist evolutionary algorithm users with the parametrisation of their algorithms. The visualisation method presents the convergence and diversity properties such that different parametrisations can be easily compared, and poor performing parameter sets can be easily identified and discarded. The efficacy of the visualisation is presented using a set of benchmark optimisation problems from the literature, as well as a benchmark water distribution network design problem. Results show that it is possible to observe the different performance caused by different parametrisations. Future work discusses the potential of this visualisation within an online tool that will enable a user to discard poor parametrisations as they execute to free up resources for better ones. | |
dc.format.extent | 547-560 | |
dc.language.iso | en | |
dc.publisher | Springer | |
dc.subject | Visualisation | |
dc.subject | Multi-objective | |
dc.subject | Water distribution network design | |
dc.subject | Optimisation | |
dc.title | Toward the Online Visualisation of Algorithm Performance for Parameter Selection | |
dc.type | conference | |
dc.type | Conference Proceeding | |
plymouth.author-url | https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000433244800038&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.date-start | 2018-04-04 | |
plymouth.date-finish | 2018-04-06 | |
plymouth.volume | 10784 | |
plymouth.publisher-url | https://doi.org/10.1007/978-3-319-77538-8 | |
plymouth.conference-name | EvoApplications 2018 | |
plymouth.publication-status | Published | |
plymouth.journal | APPLICATIONS OF EVOLUTIONARY COMPUTATION, EVOAPPLICATIONS 2018 | |
dc.identifier.doi | 10.1007/978-3-319-77538-8_38 | |
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 | |
dc.publisher.place | Parma, Italy | |
dcterms.dateAccepted | 2018-01-03 | |
dc.identifier.eissn | 1611-3349 | |
dc.rights.embargoperiod | Not known | |
rioxxterms.version | Accepted Manuscript | |
rioxxterms.versionofrecord | 10.1007/978-3-319-77538-8_38 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.type | Conference Paper/Proceeding/Abstract |