Scheduling examinations to reduce second-order conflicts
Computers and Operations Research
An effective hybrid algorithm for university course timetabling
Journal of Scheduling
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
Web Mining for Malaysia's Political Social Networks Using Artificial Immune System
Knowledge Acquisition: Approaches, Algorithms and Applications
An Artificial Immune System for recommending relevant information through political weblog
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
On the application of bio-inspired algorithms in timetabling problem
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part III
| Hi-index | 0.00 |
University timetabling problem is a very common and seemingly simple, but yet very difficult problem to solve in practice. While solution definitely exists (evidenced by the fact that we do hold classes), an automated optimal schedule is very difficult to derive at present. There were successful attempts to address this problem using heuristics search methods. However, until now, university timetabling is still largely done by hand, because a typical university setting requires numerous customized complicated constraints that are difficult to model or automate. In addition, there is a problem of certain constraints being inviolable, while others are merely desirable. This paper intends to address the university timetabling problem that is highly constrained using Artificial Immune System. Empirical study on course timetabling for the School of Computer Engineering (SCE), Nanyang Technological University (NTU), Singapore as well as the benchmark dataset provided by the Metaheuristic Network shows that our proposed approach gives better results than those obtained using the Genetic Algorithm (GA).