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A Hyper-heuristic with a Round Robin Neighbourhood Selection

  • Ahmed Kheiri
  • Ender Özcan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7832)

Abstract

An iterative selection hyper-heuristic passes a solution through a heuristic selection process to decide on a heuristic to apply from a fixed set of low level heuristics and then a move acceptance process to accept or reject the newly created solution at each step. In this study, we introduce Robinhood hyper-heuristic whose heuristic selection component allocates equal share from the overall execution time for each low level heuristic, while the move acceptance component enables partial restarts when the search process stagnates. The proposed hyper-heuristic is implemented as an extension to a public software used for benchmarking of hyper-heuristics, namely HyFlex. The empirical results indicate that Robinhood hyper-heuristic is a simple, yet powerful and general multistage algorithm performing better than most of the previously proposed selection hyper-heuristics across six different Hyflex problem domains.

Keywords

Travel Salesman Problem Problem Domain Vehicle Route Problem Iterate Local Search Heuristic Selection 
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

  • Ahmed Kheiri
    • 1
  • Ender Özcan
    • 1
  1. 1.School of Computer ScienceUniversity of NottinghamNottinghamUK

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