Effective learning hyper-heuristics for the course timetabling problem
Course timetabling constraints are converted into generic structures. Two types of learning for operator selection are contrasted: online and offline. An empirical approach for selecting the best operators from a pool is proposed. Online learning shows competitive results, even producing new best-known solutions. The proposed method achieves a balance between generality and effectiveness.
hyper-heuristics timetabling heuristics metaheuristics combinatorial optimization