<![CDATA[Rule generation in complex data analysis tasks poses challenges in terms of accuracy and interpretability. This research proposes a novel approach called Quantum-Inspired Fuzzy Genetic Programming (QIFGP) that integrates concepts from fuzzy logic, genetic programming, and quantum-inspired computing to address these challenges. The QIFGP model enhances the exploration of the solution space, increases the diversity of generated rules, and improves the accuracy and interpretability of the generated rules. The model is applied to a credit risk assessment problem, and the results are compared with traditional fuzzy logic-based approaches and genetic programming without quantum-inspired features. The experimental results demonstrate that the QIFGP model outperforms the baseline methods in terms of accuracy, achieving an accuracy of 87.5%. The generated rules exhibit a high level of interpretability, providing linguistic labels that capture meaningful relationships between the input features and risk classes. The incorporation of quantum-inspired features enables efficient exploration of the solution space while maintaining computational efficiency. The generalizability and robustness of the QIFGP model are demonstrated through consistent performance across multiple experiments and datasets. The QIFGP model offers a promising approach for enhanced rule generation in complex data analysis tasks, with potential applications in various domains where accurate and interpretable rule generation is crucial.]]>
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