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Improving Genetic Programming with Behavioral Consistency Measure

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Parallel Problem Solving from Nature – PPSN XIII (PPSN 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8672))

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Abstract

Program synthesis tasks usually specify only the desired output of a program and do not state any expectations about its internal behavior. The intermediate execution states reached by a running program can be nonetheless deemed as more or less preferred according to their information content with respect to the desired output. In this paper, a consistency measure is proposed that implements this observation. When used as an additional search objective in a typical genetic programming setting, this measure improves the success rate on a suite of 35 benchmarks in a statistically significant way.

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Author information

Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Poznań, Poland

    Krzysztof Krawiec

  2. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

    Armando Solar-Lezama

Authors
  1. Krzysztof Krawiec
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  2. Armando Solar-Lezama
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Editor information

Editors and Affiliations

  1. Faculty of Computer and Engineering Sciences, Cologne University of Applied Sciences, Steinmüllerallee 1, 51643, Gummersbach, Germany

    Thomas Bartz-Beielstein

  2. Warwick Business School, University of Warwick, CV8 2SY, Coventry, UK

    Jürgen Branke

  3. Department of Intelligent Systems, JožefStefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia

    Bogdan Filipič

  4. Department of Computer Science and Creative Technologies, University of the West of England, BS16 1QY, Bristol, UK

    Jim Smith

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© 2014 Springer International Publishing Switzerland

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Krawiec, K., Solar-Lezama, A. (2014). Improving Genetic Programming with Behavioral Consistency Measure. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds) Parallel Problem Solving from Nature – PPSN XIII. PPSN 2014. Lecture Notes in Computer Science, vol 8672. Springer, Cham. https://doi.org/10.1007/978-3-319-10762-2_43

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  • DOI: https://doi.org/10.1007/978-3-319-10762-2_43

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10761-5

  • Online ISBN: 978-3-319-10762-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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