A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem

Citation

Kalender M, Kheiri A, Ozcan E & Burke E (2012) A greedy gradient-simulated annealing hyper-heuristic for a curriculum-based course timetabling problem. In: De Wilde P, Coghill G & Kononova A (eds.) 2012 12th UK Workshop on Computational Intelligence, UKCI 2012. 2012 12th UK Workshop on Computational Intelligence (UKCI), Edinburgh, 05.09.2012-07.09.2012. Red Hook, NY: IEEE. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6335754; https://doi.org/10.1109/UKCI.2012.6335754

Abstract
The course timetabling problem is a well known constraint optimization problem which has been of interest to researchers as well as practitioners. Due to the NP-hard nature of the problem, the traditional exact approaches might fail to find a solution even for a given instance. Hyper-heuristics which search the space of heuristics for high quality solutions are alternative methods that have been increasingly used in solving such problems. In this study, a curriculum based course timetabling problem at Yeditepe University is described. An improvement oriented heuristic selection strategy combined with a simulated annealing move acceptance as a hyper-heuristic utilizing a set of low level constraint oriented neighbourhood heuristics is investigated for solving this problem. The proposed hyper-heuristic was initially developed to handle a variety of problems in a particular domain with different properties considering the nature of the low level heuristics. On the other hand, a goal of hyper-heuristic development is to build methods which are general. Hence, the proposed hyper-heuristic is applied to six other problem domains and its performance is compared to different state-of-the-art hyper-heuristics to test its level of generality. The empirical results show that the proposed method is sufficiently general and powerful.

Keywords
; Operations Research/Decision Theory; Organization/Planning; Operations research; Business logistics; Economics/Management Science; Operations Research/Decision Theory; Mathematical Programming