A Reinforcement Learning - Great-Deluge Hyper-Heuristic for Examination Timetabling

Hyper-heuristics are identified as the methodologies that search the space generated by a finite set of low level heuristics for solving difficult problems. One of the iterative hyper-heuristic frameworks requires a single candidate solution and multiple perturbative low level heuristics. An initially generated complete solution goes through two successive processes; heuristic selection and move acceptance until a set of termination criteria is satisfied. A goal of the hyper-heuristic research is to create automated techniques that are applicable to 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.

• Type: Journal
• Year: 2010
• Where: IJAMCIGI
• Authors: Ender Özcan, Mustafa Misir, Gabriela Ochoa, Edmund K. Burke
• Keywords: Heuristic Selection, Hyper-heuristic, IJAMCIGI 2010, Low Level Heuristics