A graph-based hyper-heuristic for educational timetabling problems

Burke, Edmund K.; McCollum, Barry; Meisels, Amnon; Petrović, Sanja; Qu, Rong

doi:10.1016/j.ejor.2005.08.012

Cited by 408 publications

(253 citation statements)

References 38 publications

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“…Therefore it can be much more computationally expensive than the low-level heuristics used in SAHH. (Burke et al 2007). Both best and average results are reported.…”

Section: Computational Resultsmentioning

confidence: 99%

“…Constructive hyper-heuristics construct solutions from "scratch" by intelligently calling different heuristics at different stages in the construction process. Examples of constructive hyperheuristic research can be seen in (Fisher and Thompson 1963, Kitano 1990, Hart, Ross and Nelson 1998, Burke, Petrovic and Qu 2006, Burke et al 2007). Local search hyper-heuristics start from a complete initial solution and repeatedly select appropriate heuristics to lead the search in promising new directions.…”

Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

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A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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Abstract. One of the main motivations for investigating hyper-heuristic methodologies is to provide a more general search framework than is currently available. Most of the current search techniques represent approaches that are largely adapted for specific search problems (and, in some cases, even specific problem instances). There are many real-world scenarios where the development of such bespoke systems is entirely appropriate. However, there are other situations where it would be beneficial to have methodologies which are more generally applicable to more problems. One of our motivating goals is to underpin the development of more flexible search methodologies that can be easily and automatically employed on a broader range of problems than is currently possible. Almost all the heuristics that have appeared in the literature have been designed and selected by humans. In this paper, we investigate a simulated annealing hyper-heuristic methodology which operates on a search space of heuristics and which employs a stochastic heuristic selection strategy and a short-term memory. The generality and performance of the proposed algorithm is demonstrated over a large number of benchmark data sets drawn from three very different and difficult (NP-hard) problems: nurse rostering, university course timetabling and onedimensional bin packing. Experimental results show that the proposed hyper-heuristic is able to achieve significant performance improvements over a recently proposed tabu search hyper-heuristic without lowering the level of generality. We also show that our hyper-heuristic is capable of producing competitive results against bespoke meta-heuristics methods for these problems. In some cases, the simulated annealing hyper-heuristic has even obtained considerable improvements over some of the current best problem-specific meta-heuristic approaches. The contribution of this paper is to present a method which can be readily (and automatically) applied to very different problems whilst still being able to produce results on benchmark problems which are competitive with bespoke human designed tailor made algorithms for those problems.

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“…Therefore it can be much more computationally expensive than the low-level heuristics used in SAHH. (Burke et al 2007). Both best and average results are reported.…”

Section: Computational Resultsmentioning

confidence: 99%

Section: Hyper-heuristics: An Overviewmentioning

confidence: 99%

A simulated annealing hyper-heuristic methodology for flexible decision support

et al. 2011

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“…We also make a comparison with other hyper-heuristics which produced the best results in the literature in Table 11. In comparison with the graphbased hyper-heuristic in [9], our approach performs better in all the cases reported. In addition, it performs better in 8 out of 11 cases in comparison with the hyper-heuristics investigated in [23] and [24].…”

Section: The Toronto Benchmark Resultsmentioning

confidence: 95%

“…Hyper-heuristics with constructive low-level heuristics 2. Hyper-heuristics with improvement low-level heuristics A Tabu search was developed by Burke et al [9] to optimise a search space of heuristic sequences comprised of two or more low-level heuristics. This work was extended in later research by Qu et al [25] to construct heuristic sequences which produce feasible timetables.…”

Section: Hyper-heuristics In Exam Timetablingmentioning

confidence: 99%

Adaptive selection of heuristics for improving exam timetables

Burke

Soghier

2012

Ann Oper Res

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This paper presents a hyper-heuristic approach which hybridises low-level heuristic moves to improve timetables. Exams which cause a soft-constraint violation in the timetable are ordered and rescheduled to produce a better timetable. It is observed that both the order in which exams are rescheduled and the heuristic moves used to reschedule the exams and improve the timetable affect the quality of the solution produced. After testing different combinations in a hybrid hyper-heuristic approach, the Kempe chain move heuristic and time-slot swapping heuristic proved to be the best heuristic moves to use in a hybridisation. Similarly, it was shown that ordering the exams using Saturation Degree and breaking any ties using Largest Weighted Degree produce the best results. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise the Kempe chain move and timeslot swapping heuristics in two stages. In the first stage, random heuristic sequences are generated and automatically analysed. The heuristics repeated in the best sequences are fixed while the rest are kept empty. In the second stage, sequences are generated by randomly assigning heuristics to the empty positions in an attempt to find the best heuristic sequence. Finally, the generated sequences are applied to the problem. The approach is tested on the Toronto benchmark and the exam timetabling track of the second International Timetabling Competition, to evaluate its generality. The hyper-heuristic with low-level improvement heuristics approach was found to generalise well over the two different datasets and performed comparably to the state of the art approaches.

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“…In selection hyper-heuristics, the framework is provided with a set of pre-existing (generally problem-specific) constructive heuristics, and the challenge is to select the heuristic that is somehow the most suitable for the current problem state. This type of approach has been successfully applied to hard combinatorial optimisation problems such as cutting and packing [98,112], educational timetabling [23,24,97] and production scheduling [25,43]. In the case of generation hyper-heuristics, the idea is to combine sub-components of previously existing constructive heuristics to produce new constructive heuristic.…”

Section: Hyper-heuristicsmentioning

confidence: 99%

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

Pappa

Ochoa

Hyde

et al. 2013

Genet Program Evolvable Mach

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The fields of machine meta-learning and hyper-heuristic optimisation have developed mostly independently of each other, although evolutionary algorithms (particularly genetic programming) have recently played an important role in the development of both fields. Recent work in both fields shares a common goal, that of automating as much of the algorithm design process as possible. In this paper we first provide a historical perspective on automated algorithm design, and then we discuss similarities and differences between meta-learning in the field of supervised machine learning (classification) and hyper-heuristics in the field of optimisation. This discussion focuses on the dimensions of the problem space, the algorithm space and the performance measure, as well as clarifying important issues related to different levels of automation and generality in both fields. We also discuss important research directions, challenges and foundational issues in meta-learning and hyper-heuristic research. It is important to emphasize that this paper is not a survey, as several surveys on the areas of meta-learning and hyper-heuristics (separately) have been previously published. The main contribution of the paper is to contrast meta-learning and hyper-heuristics methods and concepts, in order to 123Genet Program Evolvable Mach DOI 10.1007/s10710-013-9186-9 promote awareness and cross-fertilisation of ideas across the (by and large, nonoverlapping) different communities of meta-learning and hyper-heuristic researchers. We hope that this cross-fertilisation of ideas can inspire interesting new research in both fields and in the new emerging research area which consists of integrating those fields.

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A graph-based hyper-heuristic for educational timetabling problems

Cited by 408 publications

References 38 publications

A simulated annealing hyper-heuristic methodology for flexible decision support

A simulated annealing hyper-heuristic methodology for flexible decision support

Adaptive selection of heuristics for improving exam timetables

Contrasting meta-learning and hyper-heuristic research: the role of evolutionary algorithms

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