The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Hyper-Heuristics: An Emerging Direction in Modern Search Technology
Abstract
This chapter introduces and overviews an emerging methodology in search and optimisation. One of the key aims of these new approaches, which have been termed hyperheuristics, is to raise the level of generality at which optimisation systems can operate. An objective is that hyper-heuristics will lead to more general systems that are able to handle a wide range of problem domains rather than current meta-heuristic technology which tends to be customised to a particular problem or a narrow class of problems. Hyper-heuristics are broadly concerned with intelligently choosing the right heuristic or algorithm in a given situation. Of course, a hyper-heuristic can be (often is) a (meta-)heuristic and it can operate on (meta-)heuristics. In a certain sense, a hyper-heuristic works at a higher level when compared with the typical application of meta-heuristics to optimisation problems, i.e., a hyper-heuristic could be thought of as a (meta)-heuristic which operates on lower level (meta-)heuristics. In this chapter we will introduce the idea and give a brief history of this emerging area. In addition, we will review some of the latest work to be published in the field.
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Cites
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Greedy Randomized Adaptive Search Procedures
Resende, M. G. C., & Ribeiro, C. C. (2006) · International Series in Operations Research & Management Science · doi:10.1007/0-306-48056-5_8 · Cited by 2,310
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Squeaky Wheel OptimizationPreprint
Clements, D. P., & Joslin, D. E. (2011) · arXiv (Cornell University) · doi:10.1613/jair.561 · Cited by 221
Cited by
A graph-based hyper-heuristic for educational timetabling problems
Burke, E. K., McCollum, B., Meisels, A., Petrovic, S., & Qu, R. (2007) · European Journal of Operational Research · doi:10.1016/j.ejor.2005.08.012 · Cited by 507
Self-citationMemetic Computation—Past, Present & Future [Research Frontier
Ong, Y.-S., Lim, M., & Chen, X. (2010) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2010.936309 · Cited by 419
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-citationReinforcement Learning: A Tutorial Survey and Recent Advances
Gosavi, A. (2009) · INFORMS Journal on Computing · doi:10.1287/ijoc.1080.0305 · Cited by 326
A review on algorithms for maximum clique problems
Wu, Q., & Hao, J.-K. (2015) · European Journal of Operational Research · doi:10.1016/j.ejor.2014.09.064 · Cited by 314
Metadata
- Authors
- Burke, E., Kendall, G., Newall, J., Hart, E., Ross, P., & Schulenburg, S.
- Venue
- International Series in Operations Research & Management Science
- Publisher
- Kluwer Academic Publishers
- Published
- 2006-02-02
- Pages
- 457-474
- OpenAlex
- W1668207500
- Cited by
- 665
- References
- 13
- FWCI
- 58.59
- Type
- book chapter
- Language
- en
- Open access
- closed
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