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Evolving Bin Packing Heuristic Using Micro-Differential Evolution with Indirect Representation

  • Marco Aurelio Sotelo-Figueroa
  • Héctor José Puga Soberanes
  • Juan Martín Carpio
  • Héctor J. Fraire Huacuja
  • Laura Cruz Reyes
  • Jorge Alberto Soria Alcaraz
Part of the Studies in Computational Intelligence book series (SCI, volume 451)

Abstract

The development of low-level heuristics for solving instances of a problem is related to the knowledge of an expert. He needs to analyze several components from the problem instance and to think out an specialized heuristic for solving the instance. However if any inherent component to the instance gets changes, then the designed heuristic may not work as it used to do it. In this paper it is presented a novel approach to generated low-level heuristics; the proposed approach implements micro-Differential Evolution for evolving an indirect representation of the Bin Packing Problem. It was used the Hard28 instance, which is a well-known and referenced Bin Packing Problem instance. The heuristics obtained by the proposed approach were compared against the well know First-Fit heuristic, the results of packing that were gotten for each heuristic were analized by the statistic non-parametric test known as Wilcoxon Signed Rank test.

Keywords

Differential Evolution Cutting Plane Algorithm Hard28 Instance Donor Vector Grammatical Evolution 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marco Aurelio Sotelo-Figueroa
    • 1
  • Héctor José Puga Soberanes
    • 1
  • Juan Martín Carpio
    • 1
  • Héctor J. Fraire Huacuja
    • 2
  • Laura Cruz Reyes
    • 2
  • Jorge Alberto Soria Alcaraz
    • 1
  1. 1.Instituto Tecnologico de LeónLeónMéxico
  2. 2.Instituto Tecnologico de Ciudad MaderoCiudad MaderoMéxico

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