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A Reinforcement Learning: Great-Deluge Hyper-Heuristic for Examination Timetabling International Journal of Applied Metaheuristic Computing, 1(1), 39-59, January-March 2010 39 A Reinforcement Learning: Great-Deluge Hyper-Heuristic for Examination Timetabling Ender Özcan, University of Nottingham, UK Mustafa Mısır, Yeditepe University, Turkey Gabriela Ochoa, University of Nottingham, UK Edmund K. Burke, University of Nottingham, UK ABSTRACT Hyper-heuristics can be identified as methodologies that search the space generated by a finite set of low level heuristics for solving search problems. An iterative hyper-heuristic framework can be thought of as requiring a single candidate solution and multiple perturbation low level heuristics. An initially generated complete solu- tion goes through two successive processes (heuristic selection and move acceptance) until a set of termination criteria is satisfied. A motivating goal of hyper-heuristic research is to create automated techniques that are applicable to a wide range of problems with different characteristics. Some previous studies show that different combinations of heuristic selection and move acceptance as hyper-heuristic components might yield different performances. This study investigates whether learning heuristic selection can improve the performance of a great deluge based hyper-heuristic using an examination timetabling problem as a case study. Keywords: Exam Timetabling, Great Deluge, Hyper-Heuristics, Meta-Heuristics, Reinforcement Learning 1 INTRODUCTION et al., 2003a; Ross, 2005; Chakhlevitch et al., 2008; Özcan et al., 2008; Burke et al. 2009a, Meta-heuristics have been widely and suc- 2009b). One of the aims is to at produce more cessfully applied to many different problems. general problem solving techniques, which can However, significant development effort is potentially be applied to different problems or often needed to produce fine tuned techniques instances with little development effort. The for the particular problem or even instance idea is that a hyper-heuristic approach should that is under investigation. Hyper-heuristics able to intelligently choose an appropriate represent an increasingly popular research low-level heuristic (from a given repository direction in search and optimisation (Burke of heuristics) to be applied at any given time. Thus, in hyper-heuristics, we are interested in adaptively finding solution methods, rather than DOI: 10.4018/jamc.2010102603 Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 40 International Journal of Applied Metaheuristic Computing, 1(1), 39-59, January-March 2010 directly producing a solution for whichever reject a resultant solution (Rt). This process is search problem we are studying. repeated until a predefined stopping condition Several hyper-heuristics approaches have is met. Only problem independent information been proposed in the literature. It is possible flow is allowed between the problem domain to consider methodologies based on perturba- and hyper-heuristic layers. Unless, we specifi- tion low-level heuristics and those based on cally say otherwise, a choice hyper-heuristic construction low-level heuristics. The latter refers to a hyper-heuristic that operates on a set type builds a solution incrementally, starting of perturbation low level heuristics from this with a blank solution and using construction point onwards. Moreover, such a hyper-heuristic heuristics to gradually build a complete solution. will be denoted as heuristic selection − move They have been successfully investigated for acceptance based on its components. several combinatorial optimisation problems Great deluge is a well-known acceptance such as: bin-packing (Tereshima-Marin et al., strategy (Dueck, 1993; Burke et al., 2003). Bil- 2007), timetabling (Terashima-Marin et al., gin et al. (2007) reported that hyper-heuristics 1999; Burke et al., 2007, Qu et al., 2008), pro- formed by different combinations of heuristic duction scheduling (Vazquez-Rodriguez et al., selection and move acceptance methods might 2007), and cutting stock (Terashima-Marin et yield different performances. Moreover, simple al., 2005). On the other hand, approaches based random−great deluge delivered a similar per- on perturbation heuristics find a reasonable formance to the best approach; namely, choice initial solution by some straightforward means function – simulated annealing for examination (either randomly or using a simple construction timetabling. Obviously, simple random receives heuristic) and then use heuristics, such as shift no feedback at all during the search to improve and swap to perturb solution components with upon the heuristic selection process. Hence, in the aim of finding improved solutions. In other this study, great-deluge is preferred as the move words, they start from a complete solution and acceptance component within a choice hyper- then search or select among a set of neighbour- heuristic framework to investigate the effect hoods for better solutions. A class of the most of learning heuristic selection on its overall commonly used hyper-heuristics based on per- performance for solving the same examination turbation (improvement) low level heuristics is timetabling problem as formulated in Bilgin et the choice hyper-heuristics (heuristic selection al. (2007). The learning mechanisms, inspired methodologies). They have been applied to real by the work by Nareyek (2003), are based on world problems, such as, personnel scheduling weight adaptation. (Cowling et al., 2001; Burke et al., 2003b), timetabling (Burke et al., 2003b; Dowsland et 2 Hyper-Heuristics and Learning al., 2007), and vehicle routing problems (Pis- inger et al., 2007). In a choice hyper-heuristic Although hyper-heuristic as a term has been framework based on perturbation low level introduced recently (Denzinger et al., 1997), the heuristics, search is mostly performed using a origins of the idea date back to the early 1960s single candidate solution. Such hyper-heuristics, (Fisher et al., 1961). A hyper-heuristic operates iteratively, attempt to improve a given solution at a high level by managing or generating low throughout two consecutive phases: heuristic level heuristics which operate on the problem selection and move acceptance as illustrated domain. Meta-heuristics have been commonly in Figure 1. used as hyper-heuristics. A hyper-heuristic can conduct a single point or multi-point search. In Figure 1, a candidate solution (St) at a given time (t) is modified into a new solution Population based meta-heuristics which per- (or solutions) using a chosen heuristic (or form multi-point search, such as learning classi- heuristics). Then, a move acceptance method fier systems (Marín-Blázquez and Schulenburg, is employed to decide whether to accept or 2005), evolutionary algorithms (Cowling et al., Copyright © 2010, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. International Journal of Applied Metaheuristic Computing, 1(1), 39-59, January-March 2010 41 Figure 1. A hyper-heuristic framework based on a single point search, where St denotes a candi- th date solution at time t, Hi is the i low level heuristic, Rt is the resultant solution after applying a set of selected low level heuristics that goes into the move acceptance process return Hyper-heuristic best Heuristic Selection Move Acceptance L W St+1 = v, accept stop St Select l Apply OR St , W heuristic(s) St+1 = St reject St+1 St+1 W ={∀k,Hk∈L: Hk(St)} Problem independent information gathering, performance statistics for heuristics, etc. Domain Barrier Problem Domain Low level heuristics Representation, evaluation S0 H1 Hi Hn function, initial solution (S0), etc. 2002; Han et al., 2003; Pillay and Banzhaf, to make the right choices during the heuristic 2008), genetic programming (Keller et al., 2007; selection process. Learning can be achieved in Burke et al., 2009a), ant colony optimisation an offline or online manner. An offline learning (Cuesta-Canada et al., 2005; Chen et al., 2007) hyper-heuristic requires training over a set of have been applied to a variety of combinato- problems, before it is used to solve the unseen rial optimisation problems as hyper-heuristics. problem instances. For example, Burke et al. Distributed computing methods can also be (2006) use a case based reasoning system as a used to perform multi-point search (Rattadilok hyper-heuristic for solving course and examina- et al., 2004; Rattadilok et al., 2005; Ouelhadj tion timetabling problems. An online learning et al., 2008). Özcan et al. (2008) presented hyper-heuristic learns through the feedback different hyper-heuristic frameworks show- obtained during the search process while solving ing that a matching performance to memetic a given problem. Most of the existing online algorithms can be achieved. In this study, the learning hyper-heuristics incorporate reinforce- choice hyper-heuristic framework as presented ment learning (Kaelbling et al., 1996; Sutton in Figure 1 is studied. The primary components et al., 1998). A reinforcement learning system of such hyper-heuristics are heuristic selection interacts with the environment and changes and move acceptance. its state via a selected action in such a way as A major motivating feature of hyper-heuris- to increase some notion of long term reward. tic research is the aim to facilitate applicability Hence, a learning hyper-heuristic maintains a to different problem instances having different utility
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