Genetic Algorithm Optimization of RBF Radial Basis Function Neural Network

This article provides MATLAB source code that implements a genetic algorithm for optimizing RBF radial basis function neural networks. The code includes key components such as population initialization, fitness function calculation based on neural network performance metrics, selection operators using roulette wheel method, crossover operations with chromosome recombination, and mutation mechanisms for genetic diversity. The implementation demonstrates how to optimize RBF network parameters including center positions, widths, and connection weights through evolutionary computation. By utilizing this code, researchers can effectively enhance RBF network performance, improve prediction accuracy, and achieve better optimization results through customizable genetic algorithm parameters and neural network architecture configurations. The code structure facilitates easy modification for different optimization objectives and network architectures.