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On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming

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Genetic Programming (EuroGP 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12691))

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Abstract

Grammar-guided Genetic Programming is a common approach for program synthesis where the user’s intent is given by a set of input/output examples. For use in real-world software development, the generated programs must work on previously unseen test cases too. Therefore, we study in this work the generalizability of programs synthesized by grammar-guided GP with lexicase selection. As benchmark, we analyze proportionate and tournament selection too. We find that especially for program synthesis problems with a low output cardinality (e.g., a Boolean output) lexicase selection overfits the training cases and does not generalize well to unseen test cases. An analysis using common software metrics shows for such a problem that lexicase selection generates more complex programs with many code lines and a heavier use of control structures compared to the other studied selection methods. Nevertheless, the generalizability can be improved when we do not stop a GP run as usual after a first program is found that solves all training cases correctly, but give GP more time to find further solution candidates (also solving correctly all training cases) and select the smallest program (measured with different software metrics) out of these.

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Notes

  1. 1.

    astdump module: https://pypi.org/project/astdump/.

  2. 2.

    radon module: https://pypi.org/project/radon/.

  3. 3.

    Python grammar: https://docs.python.org/3/reference/grammar.html.

  4. 4.

    Used grammars: https://gitlab.rlp.net/dsobania/progsys-grammars.

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Authors and Affiliations

  1. Johannes Gutenberg University, Mainz, Germany

    Dominik Sobania

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  1. Dominik Sobania
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Correspondence to Dominik Sobania .

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Editors and Affiliations

  1. Queen’s University, Kingston, ON, Canada

    Ting Hu

  2. University of Coimbra, Coimbra, Portugal

    Nuno Lourenço

  3. University of Trieste, Trieste, Italy

    Eric Medvet

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Sobania, D. (2021). On the Generalizability of Programs Synthesized by Grammar-Guided Genetic Programming. In: Hu, T., Lourenço, N., Medvet, E. (eds) Genetic Programming. EuroGP 2021. Lecture Notes in Computer Science(), vol 12691. Springer, Cham. https://doi.org/10.1007/978-3-030-72812-0_9

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  • DOI: https://doi.org/10.1007/978-3-030-72812-0_9

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