Two-Phase GA-Based model to learn generalized hyper-heuristics for the 2d-cutting stock problem

Authors:
Hugo Terashima-Marín;Cláudia J. Farías-Zárate;Peter Ross;Manuel Valenzuela-Rendón
Affiliations:
Center for Intelligent Systems, Tecnológico de Monterrey, Monterrey, Nuevo León, Mexico;Center for Intelligent Systems, Tecnológico de Monterrey, Monterrey, Nuevo León, Mexico;School of Computing, Napier University, Edinburgh, UK;Center for Intelligent Systems, Tecnológico de Monterrey, Monterrey, Nuevo León, Mexico
Venue:
IBERAMIA-SBIA'06 Proceedings of the 2nd international joint conference, and Proceedings of the 10th Ibero-American Conference on AI 18th Brazilian conference on Advances in Artificial Intelligence
Year:
2006

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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.