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A Self-Adaptive Approach to Exploit Topological Properties of Different GAs’ Crossover Operators Read chapter Read first chapter

2023 | OriginalPaper | Chapter

Grammatical Evolution with Code2vec

Authors : Michał Kowalczykiewicz, Piotr Lipiński

Published in: Genetic Programming

Publisher: Springer Nature Switzerland

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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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previous chapter Memetic Semantic Genetic Programming for Symbolic Regression
next chapter Domain-Aware Feature Learning with Grammar-Guided Genetic Programming
Footnotes
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Metadata
Title
Grammatical Evolution with Code2vec
Authors
Michał Kowalczykiewicz
Piotr Lipiński
Copyright Year
2023
Book
Genetic Programming

Print ISBN: 978-3-031-29572-0

Electronic ISBN: 978-3-031-29573-7

Copyright Year: 2023

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

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