Evolving Instances for Maximizing Performance Differences of State-of-the-Art Inexact TSP Solvers

  • Jakob BossekEmail author
  • Heike Trautmann
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10079)


Despite the intrinsic hardness of the Traveling Salesperson Problem (TSP) heuristic solvers, e.g., LKH+restart and EAX+restart, are remarkably successful in generating satisfactory or even optimal solutions. However, the reasons for their success are not yet fully understood. Recent approaches take an analytical viewpoint and try to identify instance features, which make an instance hard or easy to solve. We contribute to this area by generating instance sets for couples of TSP algorithms A and B by maximizing/minimizing their performance difference in order to generate instances which are easier to solve for one solver and much harder to solve for the other. This instance set offers the potential to identify key features which allow to distinguish between the problem hardness classes of both algorithms.


TSP Instance hardness Algorithm selection Feature selection 



The authors acknowledge support from the European Center of Information Systems (ERCIS).


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Informations SystemsUniversity of MünsterMünsterGermany

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