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

Sim, Kevin; Hart, Emma; Paechter, Ben

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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

Conference Name International Conference on Parallel Problem Solving from Nature
Start Date Sep 1, 2012
End Date Sep 5, 2012
Publication Date 2012
Deposit Date Nov 2, 2012
Publicly Available Date May 16, 2017
Peer Reviewed Peer Reviewed
Volume 7492
Pages 348-357
Series Title Lecture Notes in Computer Science
Series ISSN 0302-9743
Book Title Parallel Problem Solving from Nature: PPSN XII
ISBN 978-3-642-32963-0
DOI https://doi.org/10.1007/978-3-642-32964-7_35
Keywords Hyper-heuristics; one dimensional bin packing; classifier systems; attribute evolution;
Public URL http://researchrepository.napier.ac.uk/id/eprint/5698
Publisher URL http://dx.doi.org/10.1007/978-3-642-32964-7_35
Contract Date May 16, 2017

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