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International Symposium on Search Based Software Engineering

SSBSE 2016: Search Based Software Engineering pp 224–230Cite as

API-Constrained Genetic Improvement

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9962))

Abstract

ACGI respects the Application Programming Interface whilst using genetic programming to optimise the implementation of the API. It reduces the scope for improvement but it may smooth the path to GI acceptance because the programmer’s code remains unaffected; only library code is modified. We applied ACGI to C++ software for the state-of-the-art OpenCV SEEDS superPixels image segmentation algorithm, obtaining a speed-up of up to 13.2 % (\(\pm 1.3\,\%\)) to the $50 K Challenge winner announced at CVPR 2015.

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Acknowledgement

We would like to thank Bobby R. Bruce. This work is part supported by the GGGP and DAASE [4] projects.

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Authors and Affiliations

  1. CREST, University College London, London, UK

    William B. Langdon, David R. White, Mark Harman, Yue Jia & Justyna Petke

Authors
  1. William B. Langdon
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  2. David R. White
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  3. Mark Harman
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  4. Yue Jia
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  5. Justyna Petke
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Corresponding author

Correspondence to William B. Langdon .

Editor information

Editors and Affiliations

  1. University College London, London, United Kingdom

    Federica Sarro

  2. Michigan State University , East Lansing, Michigan, USA

    Kalyanmoy Deb

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Langdon, W.B., White, D.R., Harman, M., Jia, Y., Petke, J. (2016). API-Constrained Genetic Improvement. In: Sarro, F., Deb, K. (eds) Search Based Software Engineering. SSBSE 2016. Lecture Notes in Computer Science(), vol 9962. Springer, Cham. https://doi.org/10.1007/978-3-319-47106-8_16

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  • DOI: https://doi.org/10.1007/978-3-319-47106-8_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47105-1

  • Online ISBN: 978-3-319-47106-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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