Automated Lecture Timetabling Using a Memetic Algorithm

The lecture timetabling problem is known to be a highly constrained combinatorial optimization problem. There have been many attempts to address this problem using integer programming, graph coloring and several heuristic search methods. However, since each university has its own timetable setting requirements, it is difficult to develop a general solution method. Thus, the work is generally done manually. This paper attempts to solve the lecture timetabling problem of the University of Asmara using a customized memetic algorithm that we have called ALTUMA. It is a hybrid of genetic algorithms with hill-climbing operators. The performance of ALTUMA was evaluated using data obtained from the University. Empirical results show that ALTUMA is capable of producing good results in a reasonable amount of time. Besides, the results demonstrate that incorporating local search operators with a probabilistic scheme and delta method of fitness evaluation into the memetic algorithm significantly improves the search capabilities of the algorithm.