Advancing Hyperparameter Optimization in Deep Neural Networks: A Genetic Algorithm Approach

Deep neural networks (DNNs) have shown outstanding performance in image recognition, natural language processing, and time-series prediction. However, they are very much at the mercy of the hyperparameters, which in turn makes manual tuning a very labor-intensive and computationally expensive task. In this study, we examine the use of Genetic Algorithms (GAs), which are a type of evolutionary metaheuristic, for DNNs hyperparameter optimization. We systemically encode and evolve candidate solutions, which in turn allows for the efficient traversal of large-scale complex hyperparameter spaces. We present a detailed review of recent research, propose a GA-based optimization framework, and report on the empirical improvements we observed in many deep learning tasks. In addition, we see that our proposed approach does in fact improve on accuracy, computational efficiency, and adaptability when compared to traditional tuning methods. We also consider practical applications, including image classification, time-series forecasting, and disaster risk assessment. This study further analyzes the advantages, limitations, and prospective future developments of GA-driven DNN optimization