Incorporating tabu search into memetic approach for enrolment-based course timetabling problems

This work presents a tabu search and a memetic approach to an enrolment based course timetabling problem called tabu-based memetic algorithm, the proposed approach employed crossover and mutation operators to a selected solution from the population. Then applying neighborhood structure randomly which is not in tabu-list to enhance the quality of the solution. The tabu list is used to penalize neighborhood structures that are unable to generate better solutions. We demonstrate that our approach is able to produce good quality solutions due to the ability to select more promising neighborhood structures.