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International Symposium on Search Based Software Engineering

SSBSE 2015: Search-Based Software Engineering pp 12–28Cite as

Genetic Improvement of Software for Multiple Objectives

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Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 9275))

Abstract

Genetic programming (GP) can increase computer program’s functional and non-functional performance. It can automatically port or refactor legacy code written by domain experts. Working with programmers it can grow and graft (GGGP) new functionality into legacy systems and parallel Bioinformatics GPGPU code. We review Genetic Improvement (GI) and SBSE research on evolving software.

W.B. Langdon — http://www.cs.ucl.ac.uk/staff/W.Langdon/

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Notes

  1. 1.

    Human-competitive results presented at the annual GECCO conference http://www.genetic-programming.org/combined.php.

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Acknowledgements

I am grateful for the assistance of Andrea Arcuri, Robert Feldt, Marc Schoenauer, Wes Weimer and Darrell Whitley. Tesla donated by nVidia (http://www.nvidia.com).

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  1. CREST, Department of Computer Science, University College London, Gower Street, London, WC1E 6BT, UK

    William B. Langdon

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Correspondence to William B. Langdon .

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  1. Federal University of Rio de Janeiro State, Rio de Janeiro, Brazil

    Márcio Barros

  2. Carleton University, Ottawa, Ontario, Canada

    Yvan Labiche

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Langdon, W.B. (2015). Genetic Improvement of Software for Multiple Objectives. In: Barros, M., Labiche, Y. (eds) Search-Based Software Engineering. SSBSE 2015. Lecture Notes in Computer Science(), vol 9275. Springer, Cham. https://doi.org/10.1007/978-3-319-22183-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-22183-0_2

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