Research Output

A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution.

  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

  • Type:

    Book Chapter

  • Date:

    30 November 2011

  • Publication Status:

    Published

  • Publisher

    Springer Verlag

  • DOI:

    10.1007/978-3-642-32964-7_35

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006 Special Computer Methods

Citation

Sim, K., Hart, E. & Paechter, B. (2011). A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution. In Parallel Problem Solving from Nature: PPSN XII, 348-357. Springer Verlag. doi:10.1007/978-3-642-32964-7_35. ISBN 978-3-642-32963-0

Authors

Keywords

Hyper-heuristics; one dimensional bin packing; classifier systems; attribute evolution;

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