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Memetic Computing

, Volume 6, Issue 2, pp 77–84 | Cite as

Tailoring hyper-heuristics to specific instances of a scheduling problem using affinity and competence functions

  • Abdellah SalhiEmail author
  • José Antonio Vázquez Rodríguez
Regular research paper

Abstract

Hyper-heuristics are high level heuristics which coordinate lower level ones to solve a given problem. Low level heuristics, however, are not all as competent/good as each other at solving the given problem and some do not work together as well as others. Hence the idea of measuring how good they are (competence) at solving the problem and how well they work together (their affinity). Models of the affinity and competence properties are suggested and evaluated using previous information on the performance of the simple low level heuristics. The resulting model values are used to improve the performance of the hyper-heuristic by tailoring it not only to the specific problem but the specific instance being solved. The test case is a hard combinatorial problem, namely the Hybrid Flow Shop scheduling problem. Numerical results on randomly generated as well as real-world instances are included.

Keywords

Hybrid Flow Shop Scheduling Genetic Algorithm Hyper-heuristics 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Abdellah Salhi
    • 1
    Email author
  • José Antonio Vázquez Rodríguez
    • 2
  1. 1.Department of Mathematical SciencesThe University of EssexColchesterUK
  2. 2.Department of Computer Science and Information TechnologyThe University of NottinghamNottinghamUK

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