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Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class

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Genetic Programming (EuroGP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8599))

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Abstract

Genetic Improvement (GI) is a form of Genetic Programming that improves an existing program. We use GI to evolve a faster version of a C++ program, a Boolean satisfiability (SAT) solver called MiniSAT, specialising it for a particular problem class, namely Combinatorial Interaction Testing (CIT), using automated code transplantation. Our GI-evolved solver achieves overall 17% improvement, making it comparable with average expert human performance. Additionally, this automatically evolved solver is faster than any of the human-improved solvers for the CIT problem.

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Author information

Authors and Affiliations

  1. University College London, London, UK

    Justyna Petke, Mark Harman & William B. Langdon

  2. University of Virginia, Charlottesville, VA, USA

    Westley Weimer

Authors
  1. Justyna Petke
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  2. Mark Harman
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  3. William B. Langdon
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  4. Westley Weimer
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Editor information

Editors and Affiliations

  1. University College Dublin, UCD CASL, Belfield, Dublin 4, Ireland

    Miguel Nicolau

  2. Institute of Computing Science, Poznan University of Technology, Piotowo 2, 60-965, Poznań, Poland

    Krzysztof Krawiec

  3. Faculty of Computer Science, Dalhousie University, University Avenue 6050, B3H 1W5, Halifax, NS, Canada

    Malcolm I. Heywood

  4. Instituto Superior de Estatística e Gestão de Informaticão, Universidade Nova de Lisboa, 1070-312, Lisbon, Portugal

    Mauro Castelli

  5. Departamento de Arquitectura y Tecnología de Computadores (ETS), Universidad de Granada, 18071, Granada, Spain

    Pablo García-Sánchez  & Juan J. Merelo  & 

  6. Departamento de Informática, Universidad de Jaén, 23071, Jaén, Spain

    Victor M. Rivas Santos

  7. Institute for Informatics and Digital Innovation, Edinburgh Napier University, Merchiston Campus, EH10 5DT, Edinburgh, UK

    Kevin Sim

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Petke, J., Harman, M., Langdon, W.B., Weimer, W. (2014). Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a Problem Class. In: Nicolau, M., et al. Genetic Programming. EuroGP 2014. Lecture Notes in Computer Science, vol 8599. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44303-3_12

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  • DOI: https://doi.org/10.1007/978-3-662-44303-3_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-44302-6

  • Online ISBN: 978-3-662-44303-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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