OPTIMIZATION OF FUZZY INFERENCE SYSTEMS WITH GENETIC ALGORITHMS

Abstract

This paper explores the optimization of Fuzzy Inference Systems (FIS) using Genetic Algorithms (GAs) to enhance accuracy, efficiency, and decision-making processes. FIS, widely used for handling uncertain and imprecise data, can benefit significantly from the adaptive capabilities of GAs, which mimic natural selection to search for optimal solutions in complex, multi-modal problem spaces. The integration of GAs with FIS allows for the systematic fine-tuning of parameters such as membership functions, rule bases, and fuzzy operators, leading to improved system performance. This study demonstrates that GA-optimized FIS not only achieve greater accuracy but also offer robust and reliable models for real- world applications across fields such as engineering, medicine, and finance. The paper highlights key optimization techniques, including selection, crossover, and mutation, and compares GA-optimized systems with traditional methods, showcasing the superior performance of GAs in terms of accuracy, computational efficiency, and scalability. Additionally, the research suggests that future improvements can be realized through hybrid optimization approaches and the use of parallel computing techniques. These strategies promise to further enhance the capabilities of FIS, making them more efficient and adaptable to increasingly complex decision-making tasks.