Artificial Immune Algorithms for University Timetabling

The university timetabling, examination and course, are known to be highly constrained optimization problems. Metaheuristic approaches, and their hybrids, have successfully been applied to solve the problems. This paper presents three artificial immune algorithms, the algorithms inspired by the immune system, for university timetabling; clonal selection, immune network and negative selection. The main objective is to show that the algorithms may be tailored for educational timetabling. The experimental results have shown that all algorithms have effectively produced good quality timetables. The clonal selection and negative selection are more effective than immune network in producing good quality examination timetables; while for course timetabling, the immune network and negative selection are more effective than clonal selection. A comparison with other published results has significantly shown the effectiveness of these algorithms. The main operators in artificial immune algorithms are cloning and mutation. For future work, these algorithms will be improved by considering other cloning and mutation operators.