Sparse, Continuous Policy Representations for Uniform Online Bin Packing via Regression of Interpolants

  • John H. DrakeEmail author
  • Jerry Swan
  • Geoff Neumann
  • Ender Özcan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10197)


Online bin packing is a classic optimisation problem, widely tackled by heuristic methods. In addition to human-designed heuristic packing policies (e.g. first- or best- fit), there has been interest over the last decade in the automatic generation of policies. One of the main limitations of some previously-used policy representations is the trade-off between locality and granularity in the associated search space. In this article, we adopt an interpolation-based representation which has the jointly-desirable properties of being sparse and continuous (i.e. exhibits good genotype-to-phenotype locality). In contrast to previous approaches, the policy space is searchable via real-valued optimization methods. Packing policies using five different interpolation methods are comprehensively compared against a range of existing methods from the literature, and it is determined that the proposed method scales to larger instances than those in the literature.


Hyper-heuristics Online bin packing CMA-ES Heuristic generation Sparse policy representations Metaheuristics Optimisation 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • John H. Drake
    • 1
    Email author
  • Jerry Swan
    • 2
  • Geoff Neumann
    • 3
  • Ender Özcan
    • 4
  1. 1.Operational Research GroupQueen Mary University of LondonLondonUK
  2. 2.Department of Computer ScienceUniversity of YorkYorkUK
  3. 3.Computing Science and MathematicsUniversity of StirlingStirlingUK
  4. 4.School of Computer ScienceUniversity of NottinghamNottinghamUK

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