A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Solving SAT problems with TA algorithms using constant and dynamic markov chains length
AAIM'05 Proceedings of the First international conference on Algorithmic Applications in Management
The university course timetabling problem with a three-phase approach
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
An Efficient Simulated Annealing Algorithm for Feasible Solutions of Course Timetabling
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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University Timetabling problem (UTT) has a computational complexity that grows exponentially as the size of the problem augments, then random algorithms become indispensable to solve it; among these algorithms, Simulated Annealing (SA) is one of the most efficient algorithms. However, SA obtains the optimal solution or a very good approximation one, but only when SA parameters are well tuned. SA requires an initial solution for solving UTT. Besides, analytical tuning strategies for SA in UTT have not been explored. In this paper a SA Markov tuning strategy and a heuristic to generate a feasible solution are proposed. This strategy improves the performance of SA algorithms for ETT as is shown with experimental instances taken from PATAT benchmark.