Optimizing neural network training with Genetic Algorithms

In modern society, computer plays an important role among all human beings. Through the increasing development of technology, some problems happened gradually. In order to solve and regenerate the country, individuals should test their strengths. This paper discusses how to use genetic algorithms to optimize neural network training. As an important tool of machine learning, neural networks have made remarkable achievements in dealing with complex tasks. However, the training process of neural networks involves a lot of hyperparameter adjustment and weight optimization, which often requires a lot of time and computing resources. In order to improve the efficiency and performance of neural network training, humans should introduce genetic algorithms as an optimization method. Experiments are conducted on several common datasets to compare the performance of neural network training with Genetic Algorithm optimization against the traditional method. The results indicate that using Genetic Algorithms significantly improves the convergence speed and performance of neural networks while reducing the time and effort spent on hyperparameter tuning. Neural networks optimized using the Genetic Algorithm outperform their counterparts trained under the same time frame.