An immune-based approach to university course timetabling: Immune network algorithm

The university course timetabling is known to be a highly constrained optimization problem. The main difficulty is to obtain a conflict-free schedule within a limited number of timeslots and rooms. Many different approaches, including evolutionary algorithms, tabu search, simulated annealing, and their hybrids are developed for solving many different types of course timetabling problems. The immune network algorithm, an algorithm inspired by the immune system, has successfully been applied to fault recognition, data analysis, and optimization. This paper presents an immune network algorithm for the university 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 significantly shown the effectiveness of the algorithm by producing good quality course timetables. For future work, other artificial immune algorithms, such as negative selection, will be applied to university course timetabling using the same course datasets.