Monte Carlo hyper-heuristics for examination timetabling

Automating the neighbourhood selection process in an iterative approach that uses multiple heuristics is not a trivial task. Hyper-heuristics are search methodologies that not only aim to provide a general framework for solving problem instances at different difficulty levels in a given domain, but a key goal is also to extend the level of generality so that different problems from different domains can also be solved. Indeed, a major challenge is to explore how the heuristic design process might be automated. Almost all existing iterative selection hyper-heuristics performing single point search contain two successive stages; heuristic selection and move acceptance. Different operators can be used in either of the stages. Recent studies explore ways of introducing learning mechanisms into the search process for improving the performance of hyper-heuristics. In this study, a broad empirical analysis is performed comparing Monte Carlo based hyper-heuristics for solving capacitated examination timetabling problems. One of these hyper-heuristics is an approach that overlaps two stages and presents them in a single algorithmic body. A learning heuristic selection method (L) operates in harmony with a simulated annealing move acceptance method using reheating (SA) based on some shared variables. Yet, the heuristic selection and move acceptance methods can be separated as the proposed approach respects the common selection hyper-heuristic framework. The experimental results show that simulated annealing with reheating as a hyper-heuristic move acceptance method has significant potential. On the other hand, the learning hyper-heuristic using simulated annealing with reheating move E.K. Burke · G. Kendall · E. Özcan ( ) Automated Scheduling, Optimisation and Planning Research Group, School of Computer Science, University of Nottingham, Jubilee Campus, Wollaton Road, Nottingham NG8 1BB, UK e-mail: [email protected] E.K. Burke e-mail: [email protected] G. Kendall e-mail: [email protected] M. Mısır Department of Computer Engineering, Yeditepe University, Inonu Mahallesi, Kayisdagi Caddesi, Kadikoy, Istanbul 34755, Turkey e-mail: [email protected]