Interactive timetabling system using genetic algorithms

This paper discusses a new solution to university course timetabling problems. Problems that belong to the NP-hard class are very difficult to solve using conventional optimization techniques. Our solution methodology is based on genetic algorithms which use an installed knowledge base. The knowledge here is a set of candidate partial solutions of the final solution. The proposed method is to use both a knowledge base and constraints to solve the problems efficiently. The timetables obtained can satisfy teachers' personal requests and present the advantages of past timetables. Experiments using timetables of University of Tsukuba showed that this approach is an effective solution method. The proposed method includes general techniques concerning the use of domain specific knowledge that can be applied to a variety of large-scale real-life combinatorial optimization problems.