A novel feature-based approach to characterize algorithm performance for the traveling salesperson problem

  • Olaf Mersmann
  • Bernd Bischl
  • Heike Trautmann
  • Markus Wagner
  • Jakob Bossek
  • Frank Neumann


Meta-heuristics are frequently used to tackle NP-hard combinatorial optimization problems. With this paper we contribute to the understanding of the success of 2-opt based local search algorithms for solving the traveling salesperson problem (TSP). Although 2-opt is widely used in practice, it is hard to understand its success from a theoretical perspective. We take a statistical approach and examine the features of TSP instances that make the problem either hard or easy to solve. As a measure of problem difficulty for 2-opt we use the approximation ratio that it achieves on a given instance. Our investigations point out important features that make TSP instances hard or easy to be approximated by 2-opt.


TSP 2-opt Classification Feature selection MARS 

Mathematics Subject Classifications (2010)



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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Olaf Mersmann
    • 1
  • Bernd Bischl
    • 1
  • Heike Trautmann
    • 1
  • Markus Wagner
    • 2
  • Jakob Bossek
    • 1
  • Frank Neumann
    • 2
  1. 1.Statistics FacultyTU Dortmund UniversityDortmundGermany
  2. 2.School of Computer ScienceThe University of AdelaideAdelaideAustralia

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