An analysis of representations for hyper-heuristics for the uncapacitated examination timetabling problem in a genetic programming system

Abstract

Earlier research into the examination timetabling problem focused on applying different methodologies to generate solutions to the problem. More recently research has been directed at developing hyper-heuristic systems for timetable construction. Hyper-heuristic systems are used to decide which examination to schedule next during the timetable construction process and aim at allocating those examinations that are most difficult to schedule first. This study investigates using a genetic programming based hyper-heuristic system to evolve heuristic combinations for the uncapacitated examination timetabling problem. More specifically it presents and evaluates three different representations for heuristic combinations in a genetic programming system. The performance of the genetic programming based system using the different representations is applied to three examination timetabling problems with different characteristics and the performance on these problems is compared. The results obtained are also compared to that of other hyper-heuristic systems applied to the same problems.