Non-linear great deluge with reinforcement learning for university course timetabling
This paper describes a non-linear great deluge hyper-heuristic incorporating a reinforcement learning mechanism for the selection of low-level heuristics and a non-linear great deluge acceptance criterion. The proposed hyper-heuristic deals with complete solutions, i.e. it is a solution improvement...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Book Section |
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Springer
2011
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| Online Access: | https://eprints.nottingham.ac.uk/32607/ |
| _version_ | 1848794448300867584 |
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| author | Obit, Joe Henry Landa-Silva, Dario Sevaux, Marc Ouelhadj, Djamila |
| author2 | Caserta, Marco |
| author_facet | Caserta, Marco Obit, Joe Henry Landa-Silva, Dario Sevaux, Marc Ouelhadj, Djamila |
| author_sort | Obit, Joe Henry |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | This paper describes a non-linear great deluge hyper-heuristic incorporating a reinforcement learning mechanism for the selection of low-level heuristics and a non-linear great deluge acceptance criterion. The proposed hyper-heuristic deals with complete solutions, i.e. it is a solution improvement approach not a constructive one. Two types of reinforcement learning are investigated: learning with static memory length and learning with dynamic memory length. The performance of the proposed algorithm is assessed using eleven test instances of the university course timetabling problem. The experimental results show that the non-linear great deluge hyper-heuristic performs better when using static memory than when using dynamic memory. Furthermore, the algorithm with static memory produced new best results for ?ve of the test instances while the algorithm with dynamic memory produced four best results compared to the best known results from the literature. |
| first_indexed | 2025-11-14T19:16:21Z |
| format | Book Section |
| id | nottingham-32607 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:16:21Z |
| publishDate | 2011 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-326072020-05-04T20:24:25Z https://eprints.nottingham.ac.uk/32607/ Non-linear great deluge with reinforcement learning for university course timetabling Obit, Joe Henry Landa-Silva, Dario Sevaux, Marc Ouelhadj, Djamila This paper describes a non-linear great deluge hyper-heuristic incorporating a reinforcement learning mechanism for the selection of low-level heuristics and a non-linear great deluge acceptance criterion. The proposed hyper-heuristic deals with complete solutions, i.e. it is a solution improvement approach not a constructive one. Two types of reinforcement learning are investigated: learning with static memory length and learning with dynamic memory length. The performance of the proposed algorithm is assessed using eleven test instances of the university course timetabling problem. The experimental results show that the non-linear great deluge hyper-heuristic performs better when using static memory than when using dynamic memory. Furthermore, the algorithm with static memory produced new best results for ?ve of the test instances while the algorithm with dynamic memory produced four best results compared to the best known results from the literature. Springer Caserta, Marco Voss, Stefan 2011 Book Section PeerReviewed Obit, Joe Henry, Landa-Silva, Dario, Sevaux, Marc and Ouelhadj, Djamila (2011) Non-linear great deluge with reinforcement learning for university course timetabling. In: Metaheuristics: intelligent decision making. Operations research/computer science interfaces series (50). Springer, New York. ISBN 9781441979728 Great deluge scheduling and timetabling course timetabling adaptive algorithms heuristics metaheuristics local search |
| spellingShingle | Great deluge scheduling and timetabling course timetabling adaptive algorithms heuristics metaheuristics local search Obit, Joe Henry Landa-Silva, Dario Sevaux, Marc Ouelhadj, Djamila Non-linear great deluge with reinforcement learning for university course timetabling |
| title | Non-linear great deluge with reinforcement learning for university course timetabling |
| title_full | Non-linear great deluge with reinforcement learning for university course timetabling |
| title_fullStr | Non-linear great deluge with reinforcement learning for university course timetabling |
| title_full_unstemmed | Non-linear great deluge with reinforcement learning for university course timetabling |
| title_short | Non-linear great deluge with reinforcement learning for university course timetabling |
| title_sort | non-linear great deluge with reinforcement learning for university course timetabling |
| topic | Great deluge scheduling and timetabling course timetabling adaptive algorithms heuristics metaheuristics local search |
| url | https://eprints.nottingham.ac.uk/32607/ |