Adapting immune system based algorithms for class timetabling

Class timetabling is a highly constrained problem. Metaheuristic approaches have successfully been applied to solve the problem. This paper presents three immune system based algorithms for class timetabling; clonal selection, immune network, and negative selection. The ultimate goal is to show that the immune based algorithms may be adapted as new alternatives for solving class timetabling problems. The algorithms have been implemented on benchmark datasets. Experimental results have shown that all algorithms are good optimization algorithms. The algorithms are compared based on fitness values, relative robustness, and CPU times. Tests of hypotheses have significantly shown that the immune network is more effective than the other two algorithms. All algorithms can handle the hard and soft constraints very well. The values of relative robustness have shown that the timetables produced by clonal selection are more robust compared to the other two algorithms. The recorded CPU times have revealed that the immune network has acquired the longest time on all datasets. A comparison with published results has shown that all algorithms are as good as other solution methods. For future work, these algorithms will be employed to other domains of timetabling problems.