Neutrosophic genetic algorithm and its application in clustering analysis of rock discontinuity sets

This paper presents the neutrosophic genetic algorithm (NGA) to address the research gap in the application of neutrosophic theory in conjunction with genetic algorithms. NGA introduces three distinct solution spaces—truth, falsity, and indeterminacy—enabling it to entirely encompass neutrosophic solution spaces in the operational process. Fine-tuning in the true solution space (TSS), adaptive regeneration in the false solution space (FSS), and modified crossover and mutation operations in the indeterminate solution space (ISS) enhance NGA ability to navigate away from local optima while reducing computational complexity. Evaluation against several prior algorithms based on the CEC2017 test suites demonstrates the superior performance of NGA, achieving the highest overall score of 92.11% in various problems and conditions. Sensitivity analysis of NGA parameters provides significant insights into algorithm performance variations, emphasizing the substantial impact of these parameters on the NGA’s performance. The application of NGA to optimize the K-means method for clustering analysis of rock discontinuity sets showcases its efficiency and potential for practical applications in related fields, highlighting its advantages over other methods. This research establishes NGA as an innovative and efficient approach to address imprecision, incompleteness, and uncertainty in practical data scenarios, with significant implications for future development and applications.