Evolving hyper-heuristics for the uncapacitated examination timetabling problem

This paper presents a genetic programming (GP) hyper-heuristic approach that optimizes a search space of functions to assess the difficulty of allocating an examination during the timetable construction process. Each function is a heuristic combination of lowlevel construction heuristics combined by logical operators. The approach is tested on a set of five benchmark problems of varying difficulty to evaluate its ability to generalize. The GP hyper-heuristic approach was found to generalize well over the five problems and performed comparably to other hyper-heuristic approaches combining low-level construction heuristics.