Abstract
The University course timetabling problem is known as a NP-hard problem. It is a complex problem wherein the problem size can become huge due to limited resources (e.g. amount of rooms, their capacities and number availability of lecturers) and the requirements for these resources. The university course timetabling problem involves assigning a given number of events to a limited number of timeslots and rooms under a given set of constraints; the objective is to satisfy the hard constraints and minimize the violation of soft constraints. In this paper, a Differential Evolution (DE) algorithm is proposed. DE algorithm relies on the mutation operation to reduce the convergence time while reducing the penalty cost of solution. The proposed algorithm is tested over eleven benchmark datasets (representing one large, five medium and five small problems). Experimental results show that our approach is able to generate competitive results when compared with previous available approaches. Possible extensions upon this simple approach are also discussed.
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