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Evolving Better Software Parameters

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11036))

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Abstract

Genetic improvement might be widely used to adapt existing numerical values within programs. Applying GI to embedded parameters in computer code can create new functionality. For example, CMA-ES can evolve 1024 real numbers in a GNU C library square root to implement a cube root routine for C.

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Notes

  1. 1.

    http://www.cs.ubc.ca/labs/beta/Projects/ParamILS/.

  2. 2.

    http://iridia.ulb.ac.be/irace/.

  3. 3.

    2 at the least significant part of IEEE754 double precision corresponds to \(4.44\ 10^{-16}\).

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Acknowledgements

My thanks to our EuroGP [5] anonymous reviewers.

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Authors and Affiliations

  1. CREST, Computer Science, UCL, London, WC1E 6BT, UK

    William B. Langdon & Justyna Petke

Authors
  1. William B. Langdon
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  2. Justyna Petke
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Correspondence to William B. Langdon .

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Editors and Affiliations

  1. State University of Maringá, Maringá, Brazil

    Thelma Elita Colanzi

  2. University of Sheffield, Sheffield, United Kingdom

    Phil McMinn

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Langdon, W.B., Petke, J. (2018). Evolving Better Software Parameters. In: Colanzi, T., McMinn, P. (eds) Search-Based Software Engineering. SSBSE 2018. Lecture Notes in Computer Science(), vol 11036. Springer, Cham. https://doi.org/10.1007/978-3-319-99241-9_22

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  • DOI: https://doi.org/10.1007/978-3-319-99241-9_22

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