Implementation of the fuzzy logic system rule base by self-configuring genetic programming in solving the classification problem

Fuzzy logic systems are widely used in classification problems due to their ability to handle uncertainty, imprecision, and subjectivity in data. Unlike traditional “crisp” methods, fuzzy logic allows input features and output classes to be described in terms of linguistic variables – such as “high”, “medium”, and “low” – making models more interpretable and aligned with human reasoning. A key limitation of this approach is the need to construct a rule base using expert knowledge, as well as the ambiguity in selecting appropriate membership function shapes. To address these challenges, various optimization algorithms have been developed. One such method is genetic programming, which purpose is to evolve a rule base capable of accurately capturing underlying patterns for correct classification while preserving interpretability through structural adaptation. This article explores the theoretical framework of self-configuring genetic programming combined with differential evolution for constructing fuzzy logic rule bases. It also presents their practical implementation and application to classification tasks.