Abstract
Constructing examination timetable for higher educational institutions is a very complex task due to the complexity of the issues involved. The objective of examination timetabling problem is to satisfy the hard constraints and minimize the violations of soft constraints. In this work, a tabu-based memetic approach has been applied and evaluated against the latest methodologies in the literature on standard benchmark problems. The approach hybridizes the concepts of tabu search and memetic algorithms. A tabu list is used to penalise neighbourhood structures that are unable to generate better solutions after the crossover and mutation operators have been applied to the selected solutions from the population pool. We demonstrate that our approach is able to enhance the quality of the solutions by carefully selecting the effective neighbourhood structures. Hence, some best known results have been obtained.