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Automatically Evolving Lookup Tables for Function Approximation

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Genetic Programming (EuroGP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12101))

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Abstract

Many functions, such as square root, are approximated and sped up with lookup tables containing pre-calculated values.

We introduce an approach using genetic algorithms to evolve such lookup tables for any smooth function. It provides double precision and calculates most values to the closest bit, and outperforms reference implementations in most cases with competitive run-time performance.

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Author information

Authors and Affiliations

  1. Johannes Kepler University Linz, Linz, Austria

    Oliver Krauss

  2. AIST, University of Applied Sciences Upper Austria, Wels, Austria

    Oliver Krauss

  3. CREST, Computer Science, UCL, London, UK

    William B. Langdon

Authors
  1. Oliver Krauss
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  2. William B. Langdon
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Corresponding author

Correspondence to Oliver Krauss .

Editor information

Editors and Affiliations

  1. Queen's University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

  4. Pablo de Olavide University, Seville, Spain

    Federico Divina

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Krauss, O., Langdon, W.B. (2020). Automatically Evolving Lookup Tables for Function Approximation. In: Hu, T., Lourenço, N., Medvet, E., Divina, F. (eds) Genetic Programming. EuroGP 2020. Lecture Notes in Computer Science(), vol 12101. Springer, Cham. https://doi.org/10.1007/978-3-030-44094-7_6

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  • DOI: https://doi.org/10.1007/978-3-030-44094-7_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-44093-0

  • Online ISBN: 978-3-030-44094-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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