A Re-characterization of Hyper-Heuristics

  • Jerry SwanEmail author
  • Patrick De Causmaecker
  • Simon Martin
  • Ender Özcan
Part of the Operations Research/Computer Science Interfaces Series book series (ORCS, volume 62)


Hyper-heuristics are an optimization methodology which ‘search the space of heuristics’ rather than directly searching the space of the underlying candidate-solution representation. Hyper-heuristic search has traditionally been divided into two layers: a lower problem-domain layer (where domain-specific heuristics are applied) and an upper hyper-heuristic layer, where heuristics are selected or generated. The interface between the two layers is commonly termed the “domain barrier”. Historically this interface has been defined to be highly restrictive, in the belief that this is required for generality. We argue that this prevailing conception of domain barrier is so limiting as to defeat the original motivation for hyper-heuristics. We show how it is possible to make use of domain knowledge without loss of generality and describe generalized hyper-heuristics which can incorporate arbitrary domain knowledge.


Hyper-heuristics Metaheuristics Optimization Machine learning Constraint programming 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jerry Swan
    • 1
    Email author
  • Patrick De Causmaecker
    • 2
  • Simon Martin
    • 3
  • Ender Özcan
    • 4
  1. 1.Computer ScienceUniversity of YorkYorkUK
  2. 2.Computer ScienceKU LeuvenKortrijkBelgium
  3. 3.Computer ScienceUniversity of StirlingStirlingUK
  4. 4.School of Computer ScienceUniversity of NottinghamNottinghamUK

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