A case study of algorithm selection for the traveling thief problem

  • Markus Wagner
  • Marius Lindauer
  • Mustafa Mısır
  • Samadhi Nallaperuma
  • Frank Hutter

DOI: 10.1007/s10732-017-9328-y

Cite this article as:
Wagner, M., Lindauer, M., Mısır, M. et al. J Heuristics (2017). doi:10.1007/s10732-017-9328-y


Many real-world problems are composed of several interacting components. In order to facilitate research on such interactions, the Traveling Thief Problem (TTP) was created in 2013 as the combination of two well-understood combinatorial optimization problems. With this article, we contribute in four ways. First, we create a comprehensive dataset that comprises the performance data of 21 TTP algorithms on the full original set of 9720 TTP instances. Second, we define 55 characteristics for all TPP instances that can be used to select the best algorithm on a per-instance basis. Third, we use these algorithms and features to construct the first algorithm portfolios for TTP, clearly outperforming the single best algorithm. Finally, we study which algorithms contribute most to this portfolio.


Combinatorial optimization Instance analysis Algorithm portfolio 

Funding information

Funder NameGrant NumberFunding Note
Australian Research Council
  • DE160100850
Deutsche Forschungsgemeinschaft
  • Emmy Noether grant HU 1900/2-1

Copyright information

© Her Majesty the Queen in Right of Australia 2017

Authors and Affiliations

  1. 1.Optimisation and Logistics Group, School of Computer ScienceThe University of AdelaideAdelaideAustralia
  2. 2.Institut für InformatikAlbert-Ludwigs-Universität FreiburgFreiburgGermany
  3. 3.Institute of Machine Learning and Computational IntelligenceNanjing University of Aeronautics and AstronauticsNanjingChina
  4. 4.Department of Computer ScienceUniversity of SheffieldSheffieldUK

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