dc.contributor.author | Sakal, J | |
dc.contributor.author | Fieldsend, JE | |
dc.contributor.author | Keedwell, E | |
dc.date.accessioned | 2021-04-23T09:18:53Z | |
dc.date.issued | 2021-07-07 | |
dc.description.abstract | Previous studies have employed Ant Colony Optimisation to solve the University Course Timetabling task — which requires the order
of lecture assignments to be defined for its construction graph. Various heuristic or random ordering techniques have been proposed in the literature, but uncertainty remains regarding the best approach for this. We investigate the effect that permuting assignment order
has on the quality of timetable produced. As part of this we develop a novel MAX-MIN Ant System including dynamic constraint
handling and partial function evaluations. We also explore algorithm variants with and without Local Search and employ a form
of transfer learning to identify appropriate permutations. We find that between smaller problems in the International Timetabling
Competition 2007 benchmark, timetabling performance can be improved using such an approach. However we find that we lose this performance gain when attempting to transfer to considerably larger problems — indicating that similar structures are required
when using a ‘learnt’ permutation approach in such a framework. | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.identifier.citation | GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference, 10 - 14 July 2021, Lille, France, pp. 77–78 | en_GB |
dc.identifier.doi | 10.1145/3449726.3459534 | |
dc.identifier.uri | http://hdl.handle.net/10871/125447 | |
dc.language.iso | en | en_GB |
dc.publisher | Association for Computing Machinery (ACM) | en_GB |
dc.rights | © 2021 Copyright held by the owner/author(s). Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s). | en_GB |
dc.subject | Timetabling and scheduling | en_GB |
dc.subject | ant algorithms | en_GB |
dc.subject | combinatorial optimisation | en_GB |
dc.subject | empirical study | en_GB |
dc.title | Learning assignment order in an ant colony optimiser for the university course timetabling problem | en_GB |
dc.type | Conference paper | en_GB |
dc.date.available | 2021-04-23T09:18:53Z | |
dc.identifier.isbn | 978-1-4503-8351-6 | |
dc.description | This is the author accepted manuscript. The final version is available from ACM via the DOI in this record | en_GB |
dc.rights.uri | http://www.rioxx.net/licenses/all-rights-reserved | en_GB |
dcterms.dateAccepted | 2021-03-26 | |
exeter.funder | ::Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
rioxxterms.version | AM | en_GB |
rioxxterms.licenseref.startdate | 2021-07-10 | |
rioxxterms.type | Conference Paper/Proceeding/Abstract | en_GB |
refterms.dateFCD | 2021-04-22T09:07:02Z | |
refterms.versionFCD | AM | |
refterms.dateFOA | 2021-08-18T13:22:04Z | |
refterms.panel | B | en_GB |