Iterated local search using an add and delete hyper- heuristic for university course timetablingTools Soria-Alcaraz, Jorge A., Özcan, Ender, Swan, Jerry, Kendall, Graham and Carpio, Martin (2016) Iterated local search using an add and delete hyper- heuristic for university course timetabling. Applied Soft Computing, 40 . pp. 581-593. ISSN 1872-9681 Full text not available from this repository.AbstractHyper-heuristics are (meta-)heuristics that operate at a higher level to choose or generate a set of low-level (meta-)heuristics in an attempt of solve difficult optimization problems. Iterated local search (ILS) is a well-known approach for discrete optimization, combining perturbation and hill-climbing within an iterative framework. In this study, we introduce an ILS approach, strengthened by a hyper-heuristic which generates heuristics based on a fixed number of add and delete operations. The performance of the proposed hyper-heuristic is tested across two different problem domains using real world benchmark of course timetabling instances from the second International Timetabling Competition Tracks 2 and 3. The results show that mixing add and delete operations within an ILS framework yields an effective hyper-heuristic approach.
Actions (Archive Staff Only)
|
Tools
Tools
