| Issue |
ITM Web Conf.
Volume 72, 2025
III International Workshop on “Hybrid Methods of Modeling and Optimization in Complex Systems” (HMMOCS-III 2024)
|
|
|---|---|---|
| Article Number | 05005 | |
| Number of page(s) | 11 | |
| Section | Adaptive Intelligence: Exploring Learning in Evolutionary Algorithms and Neural Networks | |
| DOI | https://doi.org/10.1051/itmconf/20257205005 | |
| Published online | 13 February 2025 | |
Optimization of regular expressions using competitive coevolutionary algorithm based on symbolic regression
Institute for Information Technologies, Federal State Budget Educational Institution of Higher Education «MIREA – Russian Technological University», Vernadsky avenue, 78, Moscow, 119454, Russia
* Corresponding author: demidova.liliya@gmail.com
The paper presents an algorithm for optimizing the structure of regular expressions of the Python programming language dialect of the re module. The optimization algorithm is implemented as a competitive coevolution algorithm based on the symbolic regression algorithm (the Gene Expression Programming algorithm will be used as an implementation of the symbolic regression algorithm). The paper proposes a pseudocode for the regular expression optimization algorithm as an abstract “black box” model, provides hyperparameters of competitive coevolution, as well as a function for assessing the suitability and reliability of individual algorithms within the coevolution. A comparative analysis of the results of running the GEP algorithms as part of coevolution and separately demonstrates the effectiveness of using coevolution as a method for optimizing regular expressions.
© The Authors, published by EDP Sciences, 2025
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