# An Evolutionary Neural Network Approach for Slopes Stability Assessment
## 3 Methodology
### Genetic Algorithms  
In a data-driven undertaking, it is common practice to employ heuristic search methods, such as GA [52], for feature selection. In fact, when compared to exhaustive search methods, which can become computationally intensive for larger datasets [52], iterative algorithms such as sequential search or evolutionary algorithms (e.g., GA) [53] are more likely to produce interesting optimization outcomes with a moderate use of computational resources. As stated in Section 2, models can be fed with a substantial number of input features (more than 50 variables). Feeding all of these attribute features to the learning models will increase the model's complexity and decrease the accuracy of its predictions, as some of these features may not be particularly relevant. However, the search space for all possible input combinations is excessively large (\(2^{50}\)). Thus, we employed a GA in this study to identify the optimal set of input features that improve EHC prediction. These modern and versatile optimization techniques stand out as some of the most potent optimization tools due to their ability to handle large search spaces with minimal computational overhead and their simplicity of interpretation and application. Supported by AI and natural selection, a GA starts by coming up with random answers to a problem, which are then improved step by step until they are optimal or close to optimal. Based on that, the GA [53] can be used to determine the subset of features [54,55] where the bits on the chromosome show whether or not the feature is present. Finding the global maximum for the objective function will yield the finest suboptimal subset. In this case, the objective function is the predictor's performance. GAs can do more than just optimize a single goal. This is very important because there is often not a single best trade-off answer, but a set of trade-offs with different goals. When addressing slope stability assessments, the capability to predict well in all four classes of the EHC classification system, and not in just one, is of the utmost importance. Due to the asymmetrical distribution of the data, which could result in a high overall performance based solely on a high level of accuracy for class \"A\" (the most common class), this characteristic is of the utmost importance. Consequently, for multi-criteria optimization tasks, optimizing a Pareto front of solutions is one of the most effective strategies. Thus, each solution is deemed non-dominated, or Pareto optimal, if none of the objectives can be improved in value without deteriorating the others [56]. In the context of slope management, all Pareto optimal solutions can be assumed to be equivalent, and the decision maker or project manager can establish the primary selection criteria for choosing one solution over another based on the characteristics of the via (e.g., urban or rural location) through which a network of slopes is connected. Since Pareto front multi-optimization requires keeping an eye on a population of solutions, population-based meta-heuristics, such as evolutionary multi-objective optimization (EMO), have become common solutions. Evolutionary computational methods, such as GA and EMO, operate by sustaining a population of individuals (potential solutions), where a chromosome represents an individual data representation of a solution and a gene represents a value position within such a representation. The design of the chromosome is a crucial aspect of adopting evolutionary approaches, as it defines the problem's search space. In this research, each chromosome is a sequence of 1 and 0 genes, with each gene indicating whether a characteristic is present (1) or absent (0). This study implements a well-known EMO search engine, the non-dominated sorting genetic algorithm-II (NSGA-II [57], to address the slope stabilization identification multi-criteria optimization problem. The NSGA-II algorithm was adopted due to two major factors.