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Soria-Alcaraz, Jorge A.; Özcan, Ender; Swan, Jerry; Kendall, Graham; Carpio, Martin (2016)
Publisher: Elsevier
Languages: English
Types: Article
Hyper-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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Figure 3: Example of o ered timeslots M onday and W ednesday T uesday and T hursday F riday t1 t2 t9 t3 t4 t10 t5 t6 t11 t7 t8 t12
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