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European Conference on Genetic Programming

EuroGP 2017: Genetic Programming pp 262–277Cite as

A Grammar Design Pattern for Arbitrary Program Synthesis Problems in Genetic Programming

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10196))

Abstract

Grammar Guided Genetic Programming has been applied to many problem domains. It is well suited to tackle program synthesis, as it has the capability to evolve code in arbitrary languages. Nevertheless, grammars designed to evolve code have always been tailored to specific problems resulting in bespoke grammars, which makes them difficult to reuse. In this study a more general approach to grammar design in the program synthesis domain is presented. The approach undertaken is to create a grammar for each data type of a language and combine these grammars for the problem at hand, without having to tailor a grammar for every single problem. The approach can be applied to arbitrary problem instances of program synthesis and can be used with any programming language. The approach is also extensible to use libraries available in a given language. The grammars presented can be applied to any grammar-based Genetic Programming approach and make it easy for researches to rerun experiments or test new problems. The approach is tested on a suite of benchmark problems and compared to PushGP, as it is the only GP system that has presented results on a wide range of benchmark problems. The object of this study is to match or outperform PushGP on these problems without tuning grammars to solve each specific problem.

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Notes

  1. 1.

    https://github.com/t-h-e/HeuristicLab.CFGGP.

  2. 2.

    https://github.com/lspector/Clojush.

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Acknowledgments

This research is based upon works supported by the Science Foundation Ireland, under Grant No. 13/IA/1850.

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Authors and Affiliations

  1. Natural Computing Research and Applications Group, School of Business, University College Dublin, Dublin, Ireland

    Stefan Forstenlechner, David Fagan, Miguel Nicolau & Michael O’Neill

Authors
  1. Stefan Forstenlechner
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  2. David Fagan
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  3. Miguel Nicolau
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  4. Michael O’Neill
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Corresponding author

Correspondence to Stefan Forstenlechner .

Editor information

Editors and Affiliations

  1. University College Dublin , Dublin, Ireland

    James McDermott

  2. Universidade Nova de Lisboa , Lisbon, Portugal

    Mauro Castelli

  3. Brno University of Technology , Brno, Czech Republic

    Lukas Sekanina

  4. Vrije Universiteit Amsterdam , Amsterdam, The Netherlands

    Evert Haasdijk

  5. University of Cádiz , Cádiz, Spain

    Pablo García-Sánchez

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Forstenlechner, S., Fagan, D., Nicolau, M., O’Neill, M. (2017). A Grammar Design Pattern for Arbitrary Program Synthesis Problems in Genetic Programming. In: McDermott, J., Castelli, M., Sekanina, L., Haasdijk, E., García-Sánchez, P. (eds) Genetic Programming. EuroGP 2017. Lecture Notes in Computer Science(), vol 10196. Springer, Cham. https://doi.org/10.1007/978-3-319-55696-3_17

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  • DOI: https://doi.org/10.1007/978-3-319-55696-3_17

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