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International Conference on Learning and Intelligent Optimization

LION 2010: Learning and Intelligent Optimization pp 266–280Cite as

Understanding TSP Difficulty by Learning from Evolved Instances

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6073))

Abstract

Whether the goal is performance prediction, or insights into the relationships between algorithm performance and instance characteristics, a comprehensive set of meta-data from which relationships can be learned is needed. This paper provides a methodology to determine if the meta-data is sufficient, and demonstrates the critical role played by instance generation methods. Instances of the Travelling Salesman Problem (TSP) are evolved using an evolutionary algorithm to produce distinct classes of instances that are intentionally easy or hard for certain algorithms. A comprehensive set of features is used to characterise instances of the TSP, and the impact of these features on difficulty for each algorithm is analysed. Finally, performance predictions are achieved with high accuracy on unseen instances for predicting search effort as well as identifying the algorithm likely to perform best.

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Author information

Authors and Affiliations

  1. School of Mathematical Sciences, Monash University, Victoria, 3800, Australia

    Kate Smith-Miles & Xin Yu Lim

  2. School of Informatics, University of Edinburgh, EH8 9AB, Edinburgh, UK

    Jano van Hemert

  3. Mathematical Institute, Oxford University, OX1 3TG, Oxford, UK

    Xin Yu Lim

Authors
  1. Kate Smith-Miles
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  2. Jano van Hemert
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  3. Xin Yu Lim
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Editor information

Editors and Affiliations

  1. ALBCOM Research Group, Universitat Politècnica de Catalunya, Omega 112, Campus Nord, Jordi Girona 1-3, 08034, Barcelona, Spain

    Christian Blum

  2. LION Research Group, Università degli Studi di Trento, Via Sommarive, 14, 38123, Povo (Trento), Italy

    Roberto Battiti

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© 2010 Springer-Verlag Berlin Heidelberg

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Smith-Miles, K., van Hemert, J., Lim, X.Y. (2010). Understanding TSP Difficulty by Learning from Evolved Instances. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_29

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  • DOI: https://doi.org/10.1007/978-3-642-13800-3_29

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-13799-0

  • Online ISBN: 978-3-642-13800-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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