A Survey of Automated Timetabling
Artificial Intelligence Review
A Hybrid Genetic Algorithm for Highly Constrained Timetabling Problems
Proceedings of the 6th International Conference on Genetic Algorithms
UTTSExam: a campus-wide university exam-timetabling system
Eighteenth national conference on Artificial intelligence
Dynamic route planning for car navigation systems using virus genetic algorithms
International Journal of Knowledge-based and Intelligent Engineering Systems
Ant algorithms for the university course timetabling problem with regard to the state-of-the-art
EvoWorkshops'03 Proceedings of the 2003 international conference on Applications of evolutionary computing
Evolutionary algorithms + domain knowledge = real-world evolutionary computation
IEEE Transactions on Evolutionary Computation
Finding Feasible Timetables Using Group-Based Operators
IEEE Transactions on Evolutionary Computation
Pheromone trail initialization with local optimal solutions in ant colony optimization
International Journal of Knowledge-based and Intelligent Engineering Systems
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This paper discusses a new solution to university course timetabling problems. The problems that belong to the NP-hard class are very difficult to solve using conventional optimization techniques. Our solution methodology is based on a genetic algorithm which uses an installed knowledge base and an infection operation. The knowledge base here is a set of candidate partial solutions of the final solution, and is built from timetables used in past years and teachers' requests relating to how they can work more efficiently. Consequently, the timetable obtained may satisfy teachers' requests and can preserve 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 problem-specific knowledge that can be applied to a variety of large-scale real-life combinatorial optimization problems.