Journal of Computational Physics
Computers and Operations Research
A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
A Tabu-Search Hyperheuristic for Timetabling and Rostering
Journal of Heuristics
Practice and Theory of Automated Timetabling V: 5th International Conference, PATAT 2004, Pittsburgh, PA, USA, August 18-20, 2004, Revised Selected Papers (Lecture Notes in Computer Science)
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
A multistage evolutionary algorithm for the timetable problem
IEEE Transactions on Evolutionary Computation
RSKT '09 Proceedings of the 4th International Conference on Rough Sets and Knowledge Technology
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Fish swarm intelligent algorithm for the course timetabling problem
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Computers and Operations Research
Design optimization with chaos embedded great deluge algorithm
Applied Soft Computing
A hybrid metaheuristic approach to the university course timetabling problem
Journal of Heuristics
Dual sequence simulated annealing with round-robin approach for university course timetabling
EvoCOP'10 Proceedings of the 10th European conference on Evolutionary Computation in Combinatorial Optimization
A graph coloring constructive hyper-heuristic for examination timetabling problems
Applied Intelligence
Dynamic job scheduling on the grid environment using the great deluge algorithm
PaCT'07 Proceedings of the 9th international conference on Parallel Computing Technologies
Population based Local Search for university course timetabling problems
Applied Intelligence
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Course Scheduling consists of assigning lecture events to a limited set of specific timeslots and rooms. The objective is to satisfy as many soft constraints as possible, while maintaining a feasible solution timetable. The most successful techniques to date require a compute-intensive examination of the solution neighbourhood to direct searches to an optimum solution. Although they may require fewer neighbourhood moves than more exhaustive techniques to gain comparable results, they can take considerably longer to achieve success. This paper introduces an extended version of the Great Deluge Algorithm for the Course Timetabling problem which, while avoiding the problem of getting trapped in local optima, uses simple Neighbourhood search heuristics to obtain solutions in a relatively short amount of time. The paper presents results based on a standard set of benchmark datasets, beating over half of the currently published best results with in some cases up to 60% of an improvement.