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Generalizing Hyper-heuristics via Apprenticeship Learning

  • Shahriar Asta
  • Ender Özcan
  • Andrew J. Parkes
  • A. Şima Etaner-Uyar
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7832)

Abstract

An apprenticeship-learning-based technique is used as a hyper-heuristic to generate heuristics for an online combinatorial problem. It observes and learns from the actions of a known-expert heuristic on small instances, but has the advantage of producing a general heuristic that works well on other larger instances. Specifically, we generate heuristic policies for online bin packing problem by using expert near-optimal policies produced by a hyper-heuristic on small instances, where learning is fast. The ”expert” is a policy matrix that defines an index policy, and the apprenticeship learning is based on observation of the action of the expert policy together with a range of features of the bin being considered, and then applying a k-means classification. We show that the generated policy often performs better than the standard best-fit heuristic even when applied to instances much larger than the training set.

Keywords

Hyper-heuristics learning by demonstration apprenticeship learning generalization 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Shahriar Asta
    • 1
  • Ender Özcan
    • 1
  • Andrew J. Parkes
    • 1
  • A. Şima Etaner-Uyar
    • 2
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamU.K.
  2. 2.Department of Computer EngineeringIstanbul Technical UniversityIstanbulTurkey

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