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Toward the Online Visualisation of Algorithm Performance for Parameter Selection

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Applications of Evolutionary Computation (EvoApplications 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10784))

Abstract

A visualisation method is presented that is intended to assist evolutionary algorithm users with the parametrisation of their algorithms. The visualisation method presents the convergence and diversity properties such that different parametrisations can be easily compared, and poor performing parameter sets can be easily identified and discarded. The efficacy of the visualisation is presented using a set of benchmark optimisation problems from the literature, as well as a benchmark water distribution network design problem. Results show that it is possible to observe the different performance caused by different parametrisations. Future work discusses the potential of this visualisation within an online tool that will enable a user to discard poor parametrisations as they execute to free up resources for better ones.

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Notes

  1. 1.

    Herein the term parameter is used to refer to algorithm parameters; decision variable is used to refer to an aspect of a solution’s design, to avoid confusion.

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Author information

Authors and Affiliations

  1. University of Exeter, Exeter, UK

    David J. Walker

  2. University of Plymouth, Plymouth, UK

    Matthew J. Craven

Authors
  1. David J. Walker
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  2. Matthew J. Craven
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Corresponding author

Correspondence to David J. Walker .

Editor information

Editors and Affiliations

  1. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

  2. Johannes Gutenberg University of Mainz, Mainz, Germany

    Paul Kaufmann

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Walker, D.J., Craven, M.J. (2018). Toward the Online Visualisation of Algorithm Performance for Parameter Selection. In: Sim, K., Kaufmann, P. (eds) Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science(), vol 10784. Springer, Cham. https://doi.org/10.1007/978-3-319-77538-8_38

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  • DOI: https://doi.org/10.1007/978-3-319-77538-8_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-77537-1

  • Online ISBN: 978-3-319-77538-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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