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Two-Phase GA-Based Model to Learn Generalized Hyper-heuristics for the 2D-Cutting Stock Problem

  • Hugo Terashima-Marín
  • Cláudia J. Farías-Zárate
  • Peter Ross
  • Manuel Valenzuela-Rendón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4140)

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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Hugo Terashima-Marín
    • 1
  • Cláudia J. Farías-Zárate
    • 1
  • Peter Ross
    • 2
  • Manuel Valenzuela-Rendón
    • 1
  1. 1.Center for Intelligent SystemsTecnológico de MonterreyMonterrey, Nuevo LeónMexico
  2. 2.School of ComputingNapier UniversityEdinburghUK

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