Artificial Immune Algorithms for University Timetabling

Abstract

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 th e problems. This paper presents three artificial immune algorithms, the al gorithms inspired by the immune system, for university timetabling; clonal s election, immune network and negative selection. The main objective is to sh ow that the algorithms may be tailored for educational timetabling. The experi mental results have shown that all algorithms have effectively produced good quality timetables. The clonal selection and negative selection are more ef fective than immune network in producing good quality examination timetables; w hile for course timetabling, the immune network and negative selection are more effective than clonal selection. A comparison with other published result s has significantly shown the effectiveness of these algorithms. The main ope rators in artificial immune algorithms are cloning and mutation. For future wor k, these algorithms will be improved by considering other cloning and mutation operators.