Optimization of RBF Neural Networks Using Adaptive Genetic Algorithm

This research focuses on improving existing algorithms through comparative analysis of simulation results between adaptive genetic algorithm-optimized RBF neural networks and particle swarm optimization-optimized RBF neural networks. The implementation includes MATLAB code with key functions for RBF network initialization, fitness evaluation using mean squared error, and adaptive mutation/crossover operations that dynamically adjust parameters based on population diversity. By evaluating the performance metrics of both algorithms, including convergence speed and solution accuracy, we assess their effectiveness in solving optimization problems. The provided executable program features modular code structure with separate functions for genetic algorithm operations (selection, crossover, mutation) and particle swarm optimization (velocity update, position update), allowing for immediate implementation and validation of algorithm performance. This study aims to provide researchers in related fields with a practical reference for selecting the most appropriate methodology when designing and optimizing neural networks, particularly through comparative analysis of evolutionary algorithm performance in neural network parameter optimization.