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

Sim, Kevin, Hart, Emma and Paechter, Ben (2012) A Hyper-Heuristic classifier for one dimensional bin packing problems: Improving classification accuracy by attribute evolution. In: Parallel Problem Solving from Nature: PPSN XII. Lecture Notes in Computer Science , 7492 . Springer Verlag, Taormina, pp. 348-357. ISBN 978-3-642-32963-0

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Abstract/Description

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

Item Type: Book Section
ISBN: 978-3-642-32963-0
Electronic ISBN: 978-3-642-32964-7
Uncontrolled Keywords: Hyper-heuristics; one dimensional bin packing; classifier systems; attribute evolution;
University Divisions/Research Centres: Edinburgh Napier University, Institute for Informatics and Digital Innovation
Dewey Decimal Subjects: 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods
Library of Congress Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Item ID: 5698
Depositing User: Computing Research
Date Deposited: 02 Nov 2012 13:39
Last Modified: 21 Oct 2013 14:56
URI: http://researchrepository.napier.ac.uk/id/eprint/5698

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