###### Abstract
Reliability methods have certain limitations when responding to complex geological conditions [30]. Traditional reliability methods (e.g., FORM/SORM), which require explicit performance functions, cannot be applied to many complex rock slope stability issues [31]. As mentioned above, many researchers have used various methods to evaluate the stability of the rock slope anchoring system, achieving favorable results. However, these methods still have certain limitations. In contrast to the limitations of the methods mentioned above, the discrete element method (DEM) [32] can simulate heterogeneous materials, such as complex soil-rock mixtures [33], large displacements in discontinuous rock masses, as well as phenomena such as contact surface sliding and separation. It can more accurately reflect the internal deformation and stress distribution of structural planes and jointed rock masses. It is particularly suitable for analyzing rock masses with prominent discontinuities, such as joints, fractures, and faults. It can effectively simulate the behavior of discontinuous media, such as jointed rock masses, under static and dynamic loads, including large deformations, relative motion of discontinuities [28], force transmission between particles and blocks [33], and the entire sliding process of rock slopes [34], including the initiation, propagation, and coalescence of fractures, as well as the movement [22], interaction, and fragmentation of the rock mass. This is crucial for accurately predicting the potential failure mechanisms of rock slopes [28; 32]. The genetic algorithm is an optimization technique that simulates the biological evolution process of natural selection [35; 36] and has proven to be a powerful optimization tool. The genetic algorithm has the ability to bypass local optima and search for global optima in a given problem [37]. It exhibits strong global search capabilities, robustness, and adaptability; is simple to implement; and can be easily parallelized [38]. Its outstanding ability in solving global optimization problems has been demonstrated as a promising approach [39]. Furthermore, genetic algorithms have also been applied to slope stability evaluation [40; 41; 42] and the determination of geotechnical mechanical parameters [43]. However, most existing methods primarily combine genetic algorithms with analytical approaches for slope stability evaluation, where GA is mainly used to optimize failure surface parameters for rapid analytical purposes. These methods are incapable of simulating the contact mechanics, fracture, sliding, and collision processes between rock blocks, as well as the coupled behavior of multiple sliding mechanisms. As outlined above, we can conclude the advantages of the discrete element method and genetic algorithm, as well as some of the shortcomings of current methods used for rock slope anchoring system stability evaluation. However, there is limited research on the combined application of the discrete element method and genetic algorithm for rock slope anchoring system stability evaluation. This paper proposes a method for evaluating the stability of the rock slope anchoring system by combining the discrete element method and the genetic algorithm. By utilizing the global optimization capabilities of the genetic algorithm and field monitoring data of rock slope anchoring projects, it is possible to rapidly and accurately determine the multiple parameters of the discrete element numerical model for the rock slope anchoring system. With the aid of the discrete element method, the behavior of discontinuous media, such as jointed rock masses, under static and dynamic loading can be effectively simulated, thereby accurately simulating the entire deformation process of the rock slope anchoring system and obtaining dynamic multi-field information for stability evaluation.