A graph coloring constructive hyper-heuristic for examination timetabling problems

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription.For more information, please contact eprints@nottingham.ac.uk Accepted by IEEE TRANSACTIONS ON CYBERNETICS, 2014 Abstract-Hyper-heuristics are search methodologies that aim to provide high quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this work, we propose a new high level strategy for the hyper-heuristic framework. The proposed high level strategy utilizes the dynamic multi-armed bandit-extreme value based rewards as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with various well-known acceptance criteria, state of the art of hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition (CHeSC) test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmarks datasets from this test suite.

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“…[45], HHitc 7 [46] and HHitc 8 [47]) and bespoke methods (Bitc 9 [48], Bitc 10 [49] and Bitc 11 [49]). …”

Section: ) the Comparison Of Gep-hh Results With The State Of The Armentioning

confidence: 99%

A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems

Sabar

Ayob

Kendall

et al. 2015

IEEE Trans. Cybern.

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112

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“…The objective was to choose the best heuristics from the most similar previous problem-solving situation to construct good solutions for the problem in hand. Sabar et al (2011) utilise hierarchical hybridisations of four low-level graph colouring heuristics for producing even orderings. A combined difficulty index is calculated by considering all the orderings and events are scheduled according to this index.…”

Section: Approaches Based On Constructive Low-level Heuristicsmentioning

confidence: 99%

Hyper-heuristics: a survey of the state of the art

Burke

Gendreau

Hyde

et al. 2013

Journal of the Operational Research Society

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968

550

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Hyper-heuristics comprise a set of approaches that are motivated (at least in part) by the goal of automating the design of heuristic methods to solve hard computational search problems. An underlying strategic research challenge is to develop more generally applicable search methodologies. The term hyper-heuristic is relatively new; it was first used in 2000 to describe heuristics to choose heuristics in the context of combinatorial optimisation. However, the idea of automating the design of heuristics is not new; it can be traced back to the 1960s. The definition of hyper-heuristics has been recently extended to refer to a search method or learning mechanism for selecting or generating heuristics to solve computational search problems. Two main hyper-heuristic categories can be considered: heuristic selection and heuristic generation. The distinguishing feature of hyper-heuristics is that they operate on a search space of heuristics (or heuristic components) rather than directly on the search space of solutions to the underlying problem that is being addressed. This paper presents a critical discussion of the scientific literature on hyper-heuristics including their origin and intellectual roots, a detailed account of the main types of approaches, and an overview of some related areas. Current research trends and directions for future research are also discussed.

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“…Within a constructive hyper-heuristic in [33], graph colouring heuristics have been utilised to calculate a difficulty index, which is used to order exams. The selected exams are then scheduled into a timeslot chosen by using a roulette wheel selection mechanism.…”

Section: The Exam Timetabling Problemsmentioning

confidence: 99%

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

Pham

Bai

et al. 2014

Appl Intell

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This paper presents a hybrid hyper-heuristic approach based on estimation distribution algorithms. The main motivation is to raise the level of generality for search methodologies. The objective of the hyper-heuristic is to produce solutions of acceptable quality for a number of optimisation problems. In this work, we demonstrate the generality through experimental results for different variants of exam timetabling problems. The hyperheuristic represents an automated constructive method that searches for heuristic choices from a given set of low-level heuristics based only on non-domain-specific knowledge. The high-level search methodology is based on a simple estimation distribution algorithm. It is capable of guiding the search to select appropriate heuristics in different problem solving situations. The probability distribution of low-level heuristics at different stages of solution construction can be used to measure their effectiveness and possibly help facilitate more intelligent hyper-heuristic search methods.

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A graph coloring constructive hyper-heuristic for examination timetabling problems

Cited by 80 publications

References 30 publications

A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems

A Dynamic Multiarmed Bandit-Gene Expression Programming Hyper-Heuristic for Combinatorial Optimization Problems

Hyper-heuristics: a survey of the state of the art

Hybridising heuristics within an estimation distribution algorithm for examination timetabling

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