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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
Full text not available from this repository. (Request a copy)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: | 02 Nov 2012 13:39 |
| URI: | http://researchrepository.napier.ac.uk/id/eprint/5698 |
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