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Adaptive Selection of Heuristics for Improving Constructed Exam Timetables

By Edmund K. Burke, Rong Qu and Amr Soghier


Abstract. This paper presents a hyper-heuristic approach which hybridises lowlevel heuristics to improve constructed timetables. The constructed timetable is analysed and the exams causing a soft-constraint violation are identified. It is observed that both the type of move performed and the order in which exams are rescheduled in the timetable affect the quality of the solution produced. After testing different combinations in a hybrid approach, the Kempe chain move heuristic and swapping timeslots proved to be the best heuristics to use in a hybridisation. Similarly, it was proved that ordering the exams using Saturation Degree and breaking any ties using Largest Weighted Degree produces the best results. Based on these observations, an iterative hybrid approach is developed to adaptively hybridise these two heuristics in two stages. In the first stage, random heuristic sequences are generated and applied to the problem. The heuristic sequences are automatically analysed. The heuristics repeated in the best sequences are fixed while the rest are randomly changed in an attempt to find the best heuristic sequence. The approach is tested on the Toronto benchmark and the exam timetabling track of the second International Timetabling Competition, to evaluate its ability to generalise. 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.

Year: 2010
OAI identifier: oai:CiteSeerX.psu:
Provided by: CiteSeerX
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