WBSGA: a web-based tool for course timetabling and scheduling
International Journal of Computers and Applications
Effect of using neural networks in GA-based school timetabling
CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
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A highly constrained combinatorial problem [1], like the timetable, can be solved by evolutionary methods [2]. In this paper, we describe an effective solution based on an alternation process involving the use of an appropriate genetic algorithm (GA) and a heuristic specific : greedy algorithm. Both algorithms are conceived to respond to constraints and objectives imposed by the timetable problem. The proposed technique guaranties always to produce a feasible timetable in a reasonable time computing by hard coding constraints which must not be broken. The present work has for object to prove that alternated GA with specific exploration technique, is a process that takes advantages in global search of feasible solutions and specific technique efficiency in local solution optimization. Classic GA is not efficient when applied to the timetable problem classified as NP-Hard. GA and greedy algorithm alternation is used so as to optimize resources distribution and thereafter to improve performances in terms of fitness values and time processing.