An Immune-Based Approach to University Course Timetabling: Negative Selection Algorithm

The university course timetabling is known to be a highly constrained optimization problem, more complex than examination timetabling. Many different approaches, including evolutionary algorithms, tabu search, simulated annealing, and their hybrids, are developed for solving the problem. The negative selection algorithm, an algorithm inspired by the immune system, has successfully been applied to detect computer viruses, tool breakage detection, anomaly detection, and network intrusion detection. This paper presents a negative selection algorithm for course timetabling with the main objective to show that the algorithm may be tailored for educational timetabling. The experimental results, using three benchmark course datasets, have shown the effectiveness of the algorithm by producing good quality course timetables. For future work, the negative selection algorithm will be compared with other immune algorithms using the same course datasets.