###### Abstract
sought an automatic and intelligent system for risk assessment [17]. They proposed a four-layer architecture for neural networks that are trained using data from failure histories and expert knowledge. The first to fourth layers are, respectively, responsible for data preprocessing and feature selection, feature extraction, failure mode classification, and risk priority assessment for all calculations. [PERSON] and [PERSON] proposed the use of Support Vector Machine (SVM) to validate the stress-strength model and improve the accuracy of FMEA [18]. In this research, SVM is trained using historical data on stress and strength, and then used to predict the probability of failure for a given system. The proposed method was evaluated using a case study of an automobile braking system and the results were compared with the results obtained using the traditional FMEA algorithm. A large number of researchers used neural network algorithms to solve industrial problems. They believe that the number of hidden layers in a neural network is an important hyperparameter that can greatly affect the performance of the model [19]. While increasing the number of hidden layers can increase the model's capacity to learn complex patterns in the data, it can also lead to overfitting, where the model becomes too complex and captures noise in the data rather than the underlying patterns [20]. Therefore, it is necessary to determine the optimal number of hidden layers in order to balance the complexity and performance of the model. This goal can be achieved through trial and error, using cross-validation, and network search techniques. Meanwhile, genetic algorithms can be easily scaled to control many hyperparameters and applied to large-scale optimization problems [21]. This is despite the fact that, with the increase of hyperparameters, the trial-and-error technique becomes time-consuming and actually impractical. However, limited studies have investigated whether the combination of GA and neural networks can be used together in various applications such as feature selection, feature extraction, and data modeling [22]. In these studies, GAs are used to search for the optimal number of hidden layers and hyperparameters in the architecture of neural networks, which ultimately lead to better performance and more accurate results. However, it is not possible to propose a single path for all the investigated subjects, and the development of machine-learning algorithms and the optimization of the network architecture, including the number of hidden layers or the number of neutrons per layer, are an ongoing investigation. Therefore, in the current research, the authors also seek to find a reliable and high-precision algorithm to identify failure modes and analyze their effects on the system. In other words, the combination of the FMEA algorithm with DNN optimized by GA, which is an extension of the traditional FMEA model. As a result, one of the innovations of this research compared to other studies, in addition to optimizing the parameters of the number of hidden layers and the number of neutrons, is to optimize the activation function among the hidden layers. In the following, the theoretical basis of traditional FMEA, DNN, and GA were explained in the Section 2. Then, the Section 3 is devoted to the description of the proposed model. The data and implementation of the proposed algorithm are reported in the Section 4. The Section 5 is dedicated to the report of the obtained results. Finally, after the Section 6, the important achievements of this research are reported.