A Hyper-Heuristic Classifier for One Dimensional Bin Packing Problems: Improving Classification Accuracy by Attribute Evolution

  • Kevin Sim
  • Emma Hart
  • Ben Paechter
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7492)


A hyper-heuristic for the one dimensional bin packing problem is presented that uses an Evolutionary Algorithm (EA) to evolve a set of attributes that characterise a problem instance. The EA evolves divisions of variable quantity and dimension that represent ranges of a bin’s capacity and are used to train a k-nearest neighbour algorithm. Once trained the classifier selects a single deterministic heuristic to solve each one of a large set of unseen problem instances. The evolved classifier is shown to achieve results significantly better than are obtained by any of the constituent heuristics when used in isolation.


Hyper-heuristics one dimensional bin packing classifier systems attribute evolution 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kevin Sim
    • 1
  • Emma Hart
    • 1
  • Ben Paechter
    • 1
  1. 1.Institute for Informatics and Digital InnovationEdinburgh Napier UniversityEdinburghUK

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