Synthesis of a Fuzzy Rule Base Based on the Results of Fuzzy Clustering of Regular Expressions Using Differential Evolution Algorithms
Abstract
The problem of synthesizing a fuzzy rule base for determining the membership of vector representations of abstract syntax trees of regular expressions to clusters based on the results of fuzzy clustering using the standard fuzzy C-means algorithm and its modifications is considered. The main objects of study are fuzzy rule bases based on the Mamdani and Sugeno algorithms, the parameters of which are optimized using evolutionary algorithms, including the classical Differential Evolution (DE) algorithm and its modifications L-SRTDE and L-SHADE-RSP. The aim of the study is to evaluate the effectiveness of differential evolution algorithms for tuning the parameters of a fuzzy rule base while taking into account the number of clusters and the structural features of regular expressions. A comparative analysis of the results of fuzzy rule base synthesis for different optimization algorithms is carried out. The quality of the synthesized models is evaluated using the F1-score metric. The results of the experimental study confirm the feasibility of using the proposed differential evolution algorithms for tuning the parameters of fuzzy rule bases that provide high accuracy in estimating the membership of regular expressions to clusters.
Full Text:
PDF (Russian)References
L. A. Demidova, V. V. Kirakovsky, and A. N. Pylkin, Decision-Making under Uncertainty, 2nd ed., revised. Moscow, 2015.
K. A. Nikitenko and A. V. Zvyagintseva, “Interpretability of neurosemantic models in their application to applied domains,” Problems of Artificial Intelligence, no. 2(37), pp. 79–90, 2025.
L. A. Demidova, Yu. S. Sokolova, and E. V. Nikulchev, “Use of fuzzy clustering algorithms ensemble for SVM classifier development,” International Review on Modelling and Simulations, vol. 8, no. 4, pp. 446–457, 2015.
L. A. Demidova and A. V. Gorchakov, “Fuzzy information discrimination measures and their application to low dimensional embedding construction in the UMAP algorithm,” Journal of Imaging, vol. 8, p. 113, 2022.
A. V. Leonenkov, Fuzzy Modeling in MATLAB and fuzzyTECH Environment. St. Petersburg: BHV-Petersburg, 2005, 736 p.
N. S. Dybulina and S. A. Tsareva, “A model for evaluating the effectiveness of quality management system processes based on the Mamdani algorithm in the MATLAB environment,” Bulletin of Nosov Magnitogorsk State Technical University, vol. 23, no. 2, pp. 148–157, 2025.
M. H. Chan, T. F. Ku, and T. T. Nguyen, “Application of the Sugeno fuzzy inference algorithm for predicting the remaining useful life of aviation radio engineering equipment,” Reliability and Quality of Complex Systems, no. 2(50), pp. 15–25, 2025.
A. F. Antipin, E. V. Antipina, and S. A. Mustafina, “A modified fuzzy controller with optimization of technological process operating parameters,” Mechatronics, Automation, Control, vol. 25, no. 7, pp. 354–361, 2024.
K. E. Dolzhenko, A. V. Kozhina, D. R. Novikov, and V. R. Kozlov, “Differential evolution algorithm for solving machine learning problems,” Scientific and Technical Bulletin of the Volga Region, no. 7, pp. 116–118, 2025.
V. Stanovov and E. Semenkin, “Success rate-based adaptive differential evolution L-SRTDE for CEC 2024 competition,” in Proc. 2024 IEEE Congress on Evolutionary Computation (CEC), Yokohama, Japan, Jun. 30–Jul. 5, 2024, pp. 1–8.
V. Stanovov, S. Akhmedova, and E. Semenkin, “NL-SHADE-RSP algorithm with adaptive archive and selective pressure for CEC 2021 numerical optimization,” in Proc. 2021 IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, Jun. 28–Jul. 1, 2021, pp. 809–816.
J. Lampinen and I. Zelinka, “Mixed integer-discrete-continuous optimization by differential evolution,” in Proc. 5th Int. Conf. on Soft Computing, 1999.
L. A. Demidova and N. A. Moroshkin, “Solving the clustering problem for vector representations of regular expressions,” Bulletin of Voronezh State Technical University, no. 4(21), pp. 50–59, 2025.
T. T. Z. Nguyen and L. V. Chernenkaya, “A factor analysis model based on fuzzy C-means clustering,” Proceedings of Tula State University, no. 1, pp. 329–337, 2023.
E. Bedalli, S. Hajrulla, R. Rada, and R. Kosova, “Fuzzy clustering approaches based on numerical optimizations of modified objective functions,” Algorithms, vol. 18, no. 6, p. 327, 2025.
J. Rada-Vilela, “The FuzzyLite libraries for fuzzy logic control,” 2018. [Online]. Available: https://fuzzylite.com
Refbacks
- There are currently no refbacks.
Abava Кибербезопасность Monetec 2026 СНЭ
ISSN: 2307-8162