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Two-Phase GA-Based Model to Learn Generalized Hyper-heuristics for the 2D-Cutting Stock Problem.
- Source :
- Advances in Artificial Intelligence - IBERAMIA-SBIA 2006; 2006, p198-207, 10p
- Publication Year :
- 2006
-
Abstract
- The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents a GA-based method that produces general hyper-heuristics that solve two-dimensional cutting stock problems. The GA uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Such hyper-heuristics, when tested with a large set of benchmark problems, produce outstanding results (optimal and near-optimal) for most of the cases. The testebed is composed of problems used in other similar studies in the literature. Some additional instances of the testbed were randomly generated. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISBNs :
- 9783540454625
- Database :
- Complementary Index
- Journal :
- Advances in Artificial Intelligence - IBERAMIA-SBIA 2006
- Publication Type :
- Book
- Accession number :
- 32882281
- Full Text :
- https://doi.org/10.1007/11874850_24