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Algorithm Configuration Landscapes:

More Benign Than Expected?

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Parallel Problem Solving from Nature – PPSN XV (PPSN 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11102))

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Abstract

Automated algorithm configuration procedures make use of powerful meta-heuristics to determine parameter settings that often substantially improve the performance of highly heuristic, state-of-the-art algorithms for prominent \(\mathcal {NP}\)-hard problems, such as the TSP, SAT and mixed integer programming (MIP). These meta-heuristics were originally designed for combinatorial optimization problems with vast and challenging search landscapes. Their use in automated algorithm configuration implies that algorithm configuration landscapes are assumed to be similarly complex; however, to the best of our knowledge no work has been done to support or reject this hypothesis. We address this gap by investigating the response of varying individual numerical parameters while fixing the remaining parameters at optimized values. We present evidence that most parameters exhibit uni-modal and often even convex responses, indicating that algorithm configuration landscapes are likely much more benign than previously believed.

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Acknowledgements

YP was supported by an NSERC Vanier Scholarship. HH acknowledges funding through an NSERC Discovery Grant, CFI JLEF funding and startup funding from Universiteit Leiden.

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Authors and Affiliations

  1. Department of Computer Science, The University of British Columbia, Vancouver, Canada

    Yasha Pushak & Holger Hoos

  2. LIACS, Universiteit Leiden, Leiden, The Netherlands

    Holger Hoos

Authors
  1. Yasha Pushak
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  2. Holger Hoos
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Corresponding author

Correspondence to Yasha Pushak .

Editor information

Editors and Affiliations

  1. Inria Saclay, Palaiseau, France

    Anne Auger

  2. University of Coimbra, Coimbra, Portugal

    Carlos M. Fonseca

  3. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  4. University of Coimbra, Coimbra, Portugal

    Penousal Machado

  5. University of Coimbra, Coimbra, Portugal

    Luís Paquete

  6. Colorado State University, Fort Collins, Colorado, USA

    Darrell Whitley

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Pushak, Y., Hoos, H. (2018). Algorithm Configuration Landscapes:. In: Auger, A., Fonseca, C., Lourenço, N., Machado, P., Paquete, L., Whitley, D. (eds) Parallel Problem Solving from Nature – PPSN XV. PPSN 2018. Lecture Notes in Computer Science(), vol 11102. Springer, Cham. https://doi.org/10.1007/978-3-319-99259-4_22

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  • DOI: https://doi.org/10.1007/978-3-319-99259-4_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-99258-7

  • Online ISBN: 978-3-319-99259-4

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