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Ross, Peter, Marin-Blazquez, Javier G, Schulenburg, Sonia and Hart, Emma (2003) Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyperheuristics. In: Genetic and Evolutionary Computation Conference (GECCO) 2003, 12th-16th July 2003, Chicago, Illinois, USA.
Full text not available from this repository. (Request a copy)Abstract/Description
The idea underlying hyper-heuristics is to discover some
combination of familiar, straightforward heuristics that performs very well across a whole range of problems. To be worthwhile, such a combination should outperform all of the constituent heuristics. In this paper we describe a novel messy-GA-based approach that learns such a heuris-
tic combination for solving one-dimensional bin-packing problems. When applied to a large set of benchmark problems, the learned procedure finds an optimal solution for nearly 80% of them, and for the rest produces an
answer very close to optimal. When compared with its own constituent heuristics, it ranks first in 98% of the problems.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Related URLs: | |
| Additional Information: | In: Proceeedings of the Genetic and Evolutionary Computation Conference (GECCO 2003), Springer Lecture Notes in Computer Science vol 2724, pp 1295-1306. |
| Uncontrolled Keywords: | Computer programming; Problem solving; Hyper-heuristics; Genetic algorithms; Bin-packing; Unidimensional; |
| University Divisions/Research Centres: | Faculty of Engineering, Computing and Creative Industries > School of Computing |
| Dewey Decimal Subjects: | 000 Computer science, information & general works > 000 Computer science, knowledge & systems > 006 Special Computer Methods 500 Science > 510 Mathematics > 514 Topology |
| Library of Congress Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA76 Computer software |
| Item ID: | 1844 |
| Depositing User: | RAE Import |
| Date Deposited: | 02 Jun 2008 15:37 |
| Last Modified: | 11 Aug 2011 15:23 |
| URI: | http://researchrepository.napier.ac.uk/id/eprint/1844 |
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