Genetic Algorithms and Grouping Problems
Genetic Algorithms and Grouping Problems
The Complexity of Timetable Construction Problems
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
A Grouping Genetic Algorithm for Graph Colouring and Exam Timetabling
PATAT '00 Selected papers from the Third International Conference on Practice and Theory of Automated Timetabling III
A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Genetic algorithms and timetabling
Advances in evolutionary computing
The effect of neighborhood structures on tabu search algorithm in solving course timetabling problem
Expert Systems with Applications: An International Journal
An informed genetic algorithm for the examination timetabling problem
Applied Soft Computing
INFORMS Journal on Computing
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
Finding Feasible Timetables Using Group-Based Operators
IEEE Transactions on Evolutionary Computation
Course timetabling using evolutionary operators
Applied Soft Computing
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Course timetabling consists in scheduling a sequence of lectures while satisfying various constraints. In this paper, we develop and study the performance of a memetic algorithm, designed to solve a variant of the course timetabling problem. Our aim here is twofold: to develop a competitive algorithm, and to investigate, more generally, the applicability of evolutionary algorithms to timetabling. To this end, an algorithm is first introduced and tested using a benchmark set. Comparison with other algorithms shows that our algorithm achieves better results in some, but not all instances, signifying strong and weak points. Subsequently, more comprehensive analyses are performed in relation with another evolutionary algorithm that uses strictly group-based operators. Ultimately, empirical results and analyses lead us to question the exclusive use of group-based evolutionary operators for timetabling problems.