GA-Optimized RBF Neural Network with Implementation Analysis

This package provides the GA-optimized RBF neural network analysis code in RAR format. The implementation includes comprehensive genetic algorithm optimization procedures for RBF neural networks, allowing researchers to study the complete optimization workflow. Through this code, users can examine how genetic algorithms optimize neural network parameters including center selection, width adjustment, and weight optimization for RBF networks. The code demonstrates practical implementation of selection, crossover, and mutation operations specifically tailored for neural network optimization. Key functions include fitness evaluation based on network performance metrics, chromosome encoding for network parameters, and iterative optimization processes. By thoroughly analyzing this implementation, developers can understand how to fine-tune RBF neural network architectures using evolutionary algorithms to achieve optimal performance on specific tasks. The package serves as an educational resource for understanding the integration of evolutionary computation with neural network design, providing insights into parameter adjustment strategies and performance optimization techniques.