Optimizing Artificial Neural Networks Using Genetic Algorithms

In this research, we employ genetic algorithms to optimize artificial neural networks for predicting SARS trend variations. Genetic algorithms represent heuristic search methodologies that simulate natural selection and genetic mechanisms to identify optimal solutions. The implementation involves training neural networks using existing data and features, while leveraging genetic algorithms to adjust network weights and architecture for improved prediction accuracy. Key algorithmic components include fitness function design based on prediction error minimization, chromosome encoding of network parameters, and genetic operators (selection, crossover, mutation) for evolutionary optimization. Through this approach, we aim to enhance the understanding of SARS development patterns, providing robust support for epidemic control and prevention measures. The code structure typically involves separate modules for neural network forward propagation, genetic algorithm operations, and fitness evaluation, with iterative optimization cycles converging toward optimal network configurations.