Optimizing Hyperparameters in Neural Networks Using Genetic Algorithms

Optimizing hyperparameters in neural networks is crucial for achieving peak performance. Traditional methods involve time-consuming trial and error, making it challenging to find the best hyperparameter settings. In this research, we introduce genetic algorithms (GAs) as an efficient approach for hyperparameter optimization. Using a real-world diabetes diagnosis dataset, we cast hyperparameter tuning as an optimization problem. We create a fitness function to assess neural network models based on essential hyperparameters, such as learning rate, beta1, beta2, and epsilon. The GA evolves a population of candidate solutions over generations, efficiently exploring the hyperparameter space. Results show that the proposed approach consistently converges to hyperparameter values enhancing model accuracy and training convergence. We analyze the convergence curve, fitness value distribution, and hyperparameter evolution. This study reveals GAs as a potent automated tool for neural network hyperparameter tuning. It saves time and computational resources, advancing model development. Insights gained improve understanding of hyperparameter impacts on neural network performance.