The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
A graph-based hyper-heuristic for educational timetabling problems
Open accessAbstract
This paper presents an investigation of a simple generic hyper-heuristic approach upon a set of widely used constructive heuristics (graph coloring heuristics) in timetabling. Within the hyper-heuristic framework, a tabu search approach is employed to search for permutations of graph heuristics which are used for constructing timetables in exam and course timetabling problems. This underpins a multi-stage hyper-heuristic where the tabu search employs permutations upon a different number of graph heuristics in two stages. We study this graph-based hyper-heuristic approach within the context of exploring fundamental issues concerning the search space of the hyper-heuristic (the heuristic space) and the solution space. Such issues have not been addressed in other hyper-heuristic research. These approaches are tested on both exam and course benchmark timetabling problems and are compared with the fine-tuned bespoke state-of-theart approaches. The results are within the range of the best results reported in the literature. The approach described here represents a significantly more generally applicable approach than the current state of the art in the literature. Future work will extend this hyper-heuristic framework by employing methodologies which are applicable on a wider range of timetabling and scheduling problems. � 2005 Elsevier B.V. All rights reserved.
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Lecture Notes in Artificial Intelligence
Brézillon, P., & Bouquet, P. (1999) · Cited by 7,473
(2003) · International Series in Operations Research & Management Science · doi:10.1007/b101874 · Cited by 3,430
New methods to color the vertices of a graph
Brélaz, D. (1979) · Communications of the ACM · doi:10.1145/359094.359101 · Cited by 1,567
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Leung, J., Kelly, L., & Anderson, J. H. (2004) · Cited by 1,226
The State of the Art of Nurse Rostering
Burke, E. K., De Causmaecker, P., Berghe, G. V., & Van Landeghem, H. (2004) · Journal of Scheduling · doi:10.1023/b:josh.0000046076.75950.0b · Cited by 943
Self-citationCase-Based Reasoning: Experiences, Lessons and Future Directions
Leake, D. B. (1996) · Cumulative Index of Computer Aided Architectural Design · Cited by 897
An upper bound for the chromatic number of a graph and its application to timetabling problems
Welsh, D. J. A. (1967) · The Computer Journal · doi:10.1093/comjnl/10.1.85 · Cited by 762
Hyper-Heuristics: An Emerging Direction in Modern Search Technology
Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., & Schulenburg, S. (2006) · International Series in Operations Research & Management Science · doi:10.1007/0-306-48056-5_16 · Cited by 665
Self-citationA Survey of Automated Timetabling
Schaerf, A. (1999) · Artificial Intelligence Review · doi:10.1023/a:1006576209967 · Cited by 654
(2005) · Choice Reviews Online · doi:10.5860/choice.42-2854b · Cited by 639
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Nurse rostering problems––a bibliographic survey
Cheang, B., Li, H., Lim, A., & Rodrigues, B. (2003) · European Journal of Operational Research · doi:10.1016/s0377-2217(03)00021-3 · Cited by 498
Recent advances in selection hyper-heuristics
Drake, J. H., Kheiri, A., Özcan, E., & Burke, E. K. (2020) · European Journal of Operational Research · doi:10.1016/j.ejor.2019.07.073 · Cited by 365
Self-citationEnsemble strategies for population-based optimization algorithms – A survey
Wu, G., Mallipeddi, R., & Suganthan, P. N. (2019) · Swarm and Evolutionary Computation · doi:10.1016/j.swevo.2018.08.015 · Cited by 270
Evolving Bin Packing Heuristics with Genetic Programming
Burke, E. K., Hyde, M. R., & Kendall, G. (2006) · Lecture Notes in Computer Science · doi:10.1007/11844297_87 · Cited by 144
Self-citationBurke, E. K., Hyde, M. R., Kendall, G., & Woodward, J. (2007) · Proceedings of the 9th annual conference on Genetic and evolutionary computation · doi:10.1145/1276958.1277273 · Cited by 121
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Metadata
- Authors
- Burke, E. K., McCollum, B., Meisels, A., Petrovic, S., & Qu, R.
- Venue
- European Journal of Operational Research
- Publisher
- Elsevier BV
- Published
- 2007-01
- Volume
- 176
- Issue
- 1
- Pages
- 177-192
- ISSN
- 0377-2217 (ISSN-L)
- OpenAlex
- W2086726443
- Cited by
- 507
- References
- 28
- FWCI
- 34.00
- Type
- journal article
- Language
- en
- Open access
- green
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
