Hybrid Graph Heuristics within a Hyper-Heuristic Approach to Exam Timetabling Problems

Burke, Edmund K.; Dror, Moshe; Petrović, Sanja; Qu, Ronghai

doi:10.1007/0-387-23529-9_6

Cited by 37 publications

(37 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[2,6,12,15,16,27]). Meta-heuristics [23] have received significant attention in the last two decades and have been very successful over a range of complex timetabling problems [29].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

Burke

McCollum

2009

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

In this paper we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences that consist of different graph colouring heuristics to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different heuristics to construct solutions step by step. Based on these sequences, we analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research.It is observed that spending the effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate good quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme which is concerned with developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. Indeed, we also show that the approach is competitive with the state of the art human produced methods . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 Abstract. In this paper we present a random iterative graph based hyper-heuristic to produce a collection of heuristic sequences that consist of different graph colouring heuristics to construct solutions of different quality. These heuristic sequences can be seen as dynamic hybridisations of different heuristics to construct solutions step by step. Based on these sequences, we analyse the way in which graph colouring heuristics are automatically hybridised. This, to our knowledge, represents a new direction in hyper-heuristic research. It is observed that spending the effort on hybridising Largest Weighted Degree with Saturation Degree at the early stage of solution construction tends to generate good quality solutions. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two graph colouring heuristics at different stages of solution construction. The overall aim here is to automate the heuristic design process, which draws upon an emerging research theme which is concerned with developing computer methods to design and adapt heuristics automatically. Experimental results on benchmark exam timetabling and graph colouring problems demonstrate the effectiveness and generality of this adaptive hybrid approach compared with previous methods on automatically generating and adapting heuristics. ...

show abstract

“…[2,6,12,15,16,27]). Meta-heuristics [23] have received significant attention in the last two decades and have been very successful over a range of complex timetabling problems [29].…”

Section: Introductionmentioning

confidence: 99%

“…[3,9,16,26,30]) and constructive strategies (e.g. [6,12,16,27]). Graph heuristics are the mostly studied low level constructive heuristics and have provided promising results on a number of timetabling problems.…”

Section: Introductionmentioning

confidence: 99%

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

Burke

McCollum

2009

European Journal of Operational Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…Other new approaches and methodologies for timetabling problems have also been studied as more problem solving experience is collected and new technologies provide new breakthroughs. These include Case-Based Reasoning (Leake, 1996) on educational timetabling (Burke, MacCarthy et al 2000, 2003&2005, Burke, Petrovic and Qu, 2006 and on nurse rostering (Beddoe and Petrovic, 2005), fuzzy methodology on exam timetabling (Asmuni, Burke and Garibaldi, 2004), and hyper-heuristics on timetabling (Burke, Kendall and Soubeiga, 2003, Gaw, Rattadilok and Kwan, 2004, Burke, Dror et al, 2005, Burke, Petrovic and Qu, 2006.…”

Section: Approaches and Techniques In Timetabling Problemsmentioning

confidence: 99%

A graph-based hyper-heuristic for educational timetabling problems

Burke

McCollum

Meisels

et al. 2007

European Journal of Operational Research

Self Cite

406

239

View full text Add to dashboard Cite

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 hyperheuristic 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-the-art 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.

show abstract

“…These include graph based sequential techniques (Sabar et al 2009a;Burke et al 2007), constraint based techniques (Merlot et al 2003;Müller 2009), local search methods including tabu search (De Smet, 2008), simulated annealing (Thompson and Dowsland 1998;Burke et al 2004;Gogos et al 2008;, population based algorithms including genetic algorithms (Ross et al 1998), ant colony optimization (Ely 2007), scatter search (Mansour et al 2011), pattern recognition based method ( Li et al 2011) and hybrid approaches (Sabar et al 2009b;Abdullah et al 2009), etc. For more details please refer to ).…”

Section: Introductionmentioning

confidence: 99%

“…Hyper-heuristics represent one of these approaches (Burke et al 2003;. The term hyper-heuristic refers to an approach that focuses on a search space of heuristics rather than a search space of solutions (Burke et al 2009a;Burke et al 2007;). Low level heuristics (e.g.…”

Section: Introductionmentioning

confidence: 99%

A graph coloring constructive hyper-heuristic for examination timetabling problems

Sabar

Ayob

et al. 2011

Appl Intell

Self Cite

View full text Add to dashboard Cite

In this work we investigate a new graph coloring constructive hyper-heuristic for solving examination timetabling problems. We utilize the hierarchical hybridizations of four low level graph coloring heuristics, these being largest degree, saturation degree, largest colored degree and largest enrolment. These are hybridized to produce four ordered lists. For each list, the difficulty index of scheduling the first exam is calculated by considering its order in all lists to obtain a combined evaluation of its difficulty. The most difficult exam to be scheduled is scheduled first (i.e. the one with the minimum difficulty index). To improve the effectiveness of timeslot selection, a roulette wheel selection mechanism is included in the algorithm to probabilistically select an appropriate timeslot for the chosen exam. We test our proposed approach on the most widely used un-capacitated Carter benchmarks and also on the recently introduced examination timetable dataset from the 2007 International Timetabling Competition. Compared against other methodologies, our results demonstrate that the graph coloring constructive hyper-heuristic produces good results and outperforms other approaches on some of the benchmark instances.

show abstract

Hybrid Graph Heuristics within a Hyper-Heuristic Approach to Exam Timetabling Problems

Cited by 37 publications

References 33 publications

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

Adaptive automated construction of hybrid heuristics for exam timetabling and graph colouring problems

A graph-based hyper-heuristic for educational timetabling problems

A graph coloring constructive hyper-heuristic for examination timetabling problems

Contact Info

Product

Resources

About