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Grammatical Evolution with Code2vec

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Genetic Programming (EuroGP 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13986))

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Abstract

This paper concerns using the code2vec vector embeddings of the source code to improve automatic source code generation in Grammatical Evolution. Focusing on a particular programming language, such as Java in the research presented, and being able to represent each Java function in the form of a continuous vector in a linear space by the code2vec model, GE gains some additional knowledge on similarities between constructed functions in the linear space instead of semantic similarities, which are harder to process. We propose a few improvements to the regular GE algorithm, including a code2vec-based initialization of the evolutionary algorithm and a code2vec-based crossover operator. Computational experiments confirm the efficiency of the approach proposed on a few typical benchmarks.

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Notes

  1. 1.

    https://github.com/jacksonpradolima/PyNeurGen.

  2. 2.

    https://zenodo.org/record/3577367/files/m1-standard.tar.gz.

  3. 3.

    https://github.com/tech-srl/code2vec.

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Acknowledgment

This work was supported by the Polish National Science Centre (NCN) under grant OPUS-18 no. 2019/35/B/ST6/04379.

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Authors and Affiliations

  1. Computational Intelligence Research Group, Institute of Computer Science, University of Wroclaw, Wrocław, Poland

    Michał Kowalczykiewicz & Piotr Lipiński

Authors
  1. Michał Kowalczykiewicz
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  2. Piotr Lipiński
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Corresponding author

Correspondence to Michał Kowalczykiewicz .

Editor information

Editors and Affiliations

  1. Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil

    Gisele Pappa

  2. Università degli studi di Torino, Turin, Italy

    Mario Giacobini

  3. Brno University of Technology, Brno, Czech Republic

    Zdenek Vasicek

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Kowalczykiewicz, M., Lipiński, P. (2023). Grammatical Evolution with Code2vec. In: Pappa, G., Giacobini, M., Vasicek, Z. (eds) Genetic Programming. EuroGP 2023. Lecture Notes in Computer Science, vol 13986. Springer, Cham. https://doi.org/10.1007/978-3-031-29573-7_14

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  • DOI: https://doi.org/10.1007/978-3-031-29573-7_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-29572-0

  • Online ISBN: 978-3-031-29573-7

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