## 1 Introduction
[PERSON] and [PERSON] proposed a comprehensive sensitivity analysis to address the problem of premature convergence caused by improper selection of control parameters in solving the coordination problem of overcurrent relays using particle swarm optimization and genetic algorithms. The analysis evaluated the impact of discrete control parameters on the performance of particle swarm optimization and genetic algorithms as well as their effects on the behavior of overcurrent relays. The results showed that particle swarm optimization algorithm is more sensitive to inertia weight and population size, and genetic algorithm produces faster convergence speed in the case of 30% crossover, 2% mutation, and small population size. The performance of genetic algorithm was improved by optimizing the fitness function, and the sensitivity of particle swarm optimization parameter settings was verified to be better than that of genetic algorithm [10]. [PERSON] et al. proposed an improved real coding genetic algorithm to solve bridge model optimization problems by developing a new dynamic mutation operator. The algorithm combines crossover and mutation operators, dynamically adjusting the mutation mode during the optimization process to improve the convergence speed and search efficiency of the global optimal solution. The performance of the algorithm and its effectiveness in bridge model optimization were verified through various testing problems [11]. [PERSON] et al. applied a backpropagation neural network model based on genetic algorithm to cost estimation. After training and evaluating the performance of the model using actual engineering data, it was shown that the BPN model using GA was more effective and accurate in building cost estimation than the BPN model using trial and error method [12]. [PERSON] explored an engineering investment estimation method based on the BP neural network model, which has a highly nonlinear relationship with the main characteristic factors of the project. By verifying the quantification and prediction of statistical data of existing railway bridge projects, it was shown that the model meets the requirements for rapid estimation of engineering investment [13]. [PERSON] et al. introduced the GA-BP model into the cost estimation of highway engineering to address the existing problems. They verified through examples that the prediction accuracy of the GA-BP algorithm is higher than that of the BP neural network, and it is feasible and effective [14]. [PERSON] and [PERSON] proposed a genetic algorithm (GA) to optimize the input weights and hidden layer thresholds of extreme learning machines (ELMs). Through training and validation of urban rail transit engineering sample data, it was shown that the accuracy and stability of this estimation model were significantly improved compared to other estimation models [15]. [PERSON] et al. proposed a BP neural network model based on the Tianniu Xu search algorithm. After conducting investment estimation analysis on typical assembly cases, it was shown that the BAS-BP network model had higher prediction accuracy than the BP neural network model, and the results were more stable [16]. From the above literature analysis, it can be seen that the current research on investment estimation of prefabricated buildings is a combination of quantitative methods and machine learning or system dynamics methods, and most of the research on BP neural networks is focused on transportation rail and traditional buildings. Meanwhile, genetic algorithms also demonstrate their performance advantages by improving or combining them with other optimization methods. Therefore, based on the profound theoretical foundation of BP neural network and genetic algorithm, this article proposes a genetic algorithm-optimized BP neural network for prefabricated building investment estimation prediction model.