Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
A MAX-MIN Ant System for the University Course Timetabling Problem
ANTS '02 Proceedings of the Third International Workshop on Ant Algorithms
Analytically tuned parameters of simulated annealing for the timetabling problem
CIMMACS'07 Proceedings of the 6th WSEAS international conference on Computational intelligence, man-machine systems and cybernetics
Application of the grouping genetic algorithm to university course timetabling
EvoCOP'05 Proceedings of the 5th European conference on Evolutionary Computation in Combinatorial Optimization
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
| Hi-index | 0.00 |
Course Timetabling Problem (CTP) is a well known NP hard problem. Many classical randomized algorithms (as Genetic Algorithms, Simulated Annealing and Tabu Search) have been devised for this problem. For the previous PATAT benchmark, many of these old algorithms were able to find not only feasible solutions but even the optimal one. However, new harder CTP instances have recently proposed, which to obtain a feasible solution is a very hard challenge, and the previous algorithms do not perform well with these instances. Therefore, new algorithms for CTP should be devised. In this paper a new Simulating Annealing (SA) algorithm for CTP is presented. The algorithm shows a good performance not only with the old CTP instances but also with the new ones. This new SA implementation is able to find a feasible solution in instances where no other algorithm in the literature has been reported a success.