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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)

Abstract

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.

Keywords

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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