Learning a procedure that can solve hard bin-packing problems: a new GA-based approach to hyperheuristics.

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.

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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)
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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 14:37
Last Modified: 10 Feb 2016 16:45

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