Genetic Algorithm Optimized RBF Network Approximation

This document details a Radial Basis Function (RBF) network approximation program optimized using genetic algorithms. The core methodology involves employing genetic algorithms to identify optimal parameters within the RBF network architecture, thereby enhancing approximation accuracy. The implementation follows a systematic approach: initially, the genetic algorithm generates a population of candidate parameters (including center positions, widths, and connection weights), which are then utilized to initialize and train the RBF network. Through iterative genetic operations—selection, crossover, and mutation—the program progressively refines these parameters to minimize approximation error. Key functions typically include fitness evaluation based on mean squared error, chromosome encoding of network parameters, and gradient-free optimization capabilities. This hybrid approach combines the global search capability of genetic algorithms with the local approximation strength of RBF networks, resulting in more robust and accurate function approximation. Researchers can further explore variations in genetic operator design, constraint handling mechanisms, and multi-objective optimization extensions to enhance performance.