Memetic algorithms for solving university course timetabling problem

In this paper we propose two memetic algorithms for the university course timetabling problem. First by using graph coloring heuristics a new crossover method is proposed. Then to hybridize the genetic algorithm, a local search method with a mechanism similar to hill climbing is defined. The proposed memetic algorithms are applied on some datasets and their performance are compared with each other. The obtained results demonstrate that the first memetic algorithm has a better performance than the second one. Also a saturation degree heuristic is utilized in the crossover which improves the performances of the memetic algorithms. Comparison of the obtained results with the results reported in the literature demonstrates the comparability of our proposed algorithms with already existing algorithms.