Optimization of regular expressions using competitive coevolutionary algorithm based on symbolic regression
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 Programmin...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/03/itmconf_hmmocs-III2024_05005.pdf |
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| Summary: | 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. |
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| ISSN: | 2271-2097 |