Evolving construction heuristics for the curriculum based university course timetabling problem

Abstract

In solving combinatorial optimization problems construction heuristics are generally used to create an initial solution which is improved using optimization techniques like genetic algorithms. These construction heuristics are usually derived by humans and this is usually quite a time consuming task. Furthermore, according to the no free lunch theorem different heuristics are effective for different problem instances. Ideally we would like to derive construction heuristics for different problem instances or classes of problems. However, due to the time it takes to manually derive construction heuristics it is generally not feasible to induce problem instance specific heuristics. The research presented in the paper forms part of the initiative aimed at automating the derivation of construction heuristics. Genetic programming is used to evolve construction heuristics for the curriculum based university course timetabling (CB-CTT) problem. Each heuristic is a hierarchical combination of problem characteristics and a period selection heuristic. The paper firstly presents and analyses the performance of known construction heuristics for CB-CTT. The analysis has shown that different heuristics are effective for different problem instances. The paper then presents the genetic programming approach for the automated induction of construction heuristics for the CB-CTT problem and evaluates the approach on the ITC 2007 problem instances for the second international timetabling competition. The evolved heuristics performed better than the known construction heuristics, producing timetables with lower soft constraint costs.