The Impact of Automated Algorithm Configuration on the Scaling Behaviour of State-of-the-Art Inexact TSP Solvers

  • Zongxu MuEmail author
  • Holger H. HoosEmail author
  • Thomas Stützle
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10079)


Automated algorithm configuration is a powerful and increasingly widely used approach for improving the performance of algorithms for computationally hard problems. In this work, we investigate the impact of automated algorithm configuration on the scaling of the performance of two prominent inexact solvers for the travelling salesman problem (TSP), EAX and LKH. Using a recent approach for analysing the empirical scaling of running time as a function of problem instance size, we demonstrate that automated configuration impacts significantly the scaling behaviour of EAX. Specifically, by automatically configuring the adaptation of a key parameter of EAX with instance size, we reduce the scaling of median running time from root-exponential (of the form \(a\cdot b^{\sqrt{n}}\)) to polynomial (of the form \(a\cdot n^{b}\)), and thus, achieve an improvement in the state of the art in inexact TSP solving. In our experiments with LKH, we noted overfitting on the sets of training instances used for configuration, which demonstrates the need for more sophisticated configuration protocols for scaling behaviour.


Travel Salesperson Problem Bootstrap Confidence Interval Instance Size Travel Salesperson Problem Small Instance Size 
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.



HH and ZM acknowledge support through an NSERC Discovery Grant. TS acknowledges support from the Belgian F.R.S.-FNRS, of which he is a senior research associate. This work received support from Compute Canada/Westgrid and from the COMEX project within the Interuniversity Attraction Poles Programme of the Belgian Science Policy Office.


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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada
  2. 2.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium

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