An Explainable Visualisation of the Evolutionary Search Process
dc.contributor.author | Walter, Mathew | |
dc.contributor.author | Walker, David | |
dc.contributor.author | Craven, Matthew | |
dc.date.accessioned | 2022-05-03T15:50:40Z | |
dc.date.issued | 2022-07-19 | |
dc.identifier.isbn | 9781450392686 | |
dc.identifier.uri | http://hdl.handle.net/10026.1/19155 | |
dc.description.abstract |
The comprehension of the Evolutionary Algorithm (EA) search process is often eluded by challenges of transparency inherent to \textit{black-box} EAs, thus affecting algorithm enhancement and hyper-parameter optimisation. In this work, we develop algorithm insight by introducing the Population Dynamics Plot (PopDP). PopDP is a novel and intuitive visualisation capable of visualising the population of solutions, the parent-offspring lineage, solution perturbation operators, and the search process journey. We apply PopDP to NSGA-II to demonstrate the insight attained and the effectiveness of PopDP for visualising algorithm search on a series of discrete dual- and many-objective knapsack problems of different complexities, and our results demonstrate that the method can be used to produce a visualisation in which the lineage of solutions can be clearly seen. We also consider the efficacy of the proposed explainable visualisation against emerging approaches to benchmarking explainable AI methods and consider the accessibility of the resulting visualisations. | |
dc.format.extent | 1794-1802 | |
dc.language.iso | en | |
dc.publisher | Association for Computing Machinery | |
dc.subject | Evolutionary Computation | |
dc.subject | Explainability | |
dc.subject | Many-Objective Optimisation | |
dc.subject | Multi-Objective Optimisation | |
dc.subject | Visualisation | |
dc.title | An Explainable Visualisation of the Evolutionary Search Process | |
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:001035469400281&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=11bb513d99f797142bcfeffcc58ea008 | |
plymouth.date-start | 2022-07-09 | |
plymouth.date-finish | 2022-07-13 | |
plymouth.conference-name | ECXAI — Evolutionary Computation and Explainable AI Workshop of GECCO 2022 | |
plymouth.publication-status | Published | |
plymouth.journal | GECCO'22: Proceedings of the Genetic and Evolutionary Computation Conference Companion | |
dc.identifier.doi | 10.1145/3520304.3533984 | |
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.dateAccepted | 2022-04-25 | |
dc.rights.embargodate | 2022-7-22 | |
rioxxterms.version | Accepted Manuscript | |
rioxxterms.versionofrecord | 10.1145/3520304.3533984 | |
rioxxterms.licenseref.uri | http://www.rioxx.net/licenses/all-rights-reserved | |
rioxxterms.licenseref.startdate | 2022-07-09 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract |