An Investigation of Selection Hyper-heuristics in Dynamic Environments

  • Berna Kiraz
  • A. Şima Uyar
  • Ender Özcan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6624)


Hyper-heuristics are high level methodologies that perform search over the space of heuristics rather than solutions for solving computationally difficult problems. A selection hyper-heuristic framework provides means to exploit the strength of multiple low level heuristics where each heuristic can be useful at different stages of the search. In this study, the behavior of a range of selection hyper-heuristics is investigated in dynamic environments. The results show that hyper-heuristics embedding learning heuristic selection methods are sufficiently adaptive and can respond to different types of changes in a dynamic environment.


Reinforcement Learning Dynamic Environment Choice Function Simple Random Heuristic Selection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Berna Kiraz
    • 1
  • A. Şima Uyar
    • 2
  • Ender Özcan
    • 3
  1. 1.Institute of Science and TechnologyIstanbul Technical UniversityTurkey
  2. 2.Faculty of Computer and InformaticsIstanbul Technical UniversityTurkey
  3. 3.School of Computer ScienceUniversity of NottinghamUK

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