Optimizing Wavelet Neural Networks Using Genetic Algorithm

This documentation presents a complete implementation of wavelet neural networks optimized through genetic algorithms for function approximation tasks. The program employs genetic algorithm operators (selection, crossover, mutation) to optimize critical wavelet neural network parameters including scaling factors, translation parameters, and connection weights. The optimization process systematically explores parameter combinations to minimize approximation error between network output and target function values. Key implementation aspects include chromosome encoding of network parameters, fitness evaluation based on mean squared error, and elitism preservation strategy. This approach enhances convergence speed and solution quality compared to traditional training methods, demonstrating improved performance in nonlinear function approximation while maintaining the time-frequency localization advantages of wavelet transforms. The complete code structure includes modular functions for wavelet basis generation, genetic operation implementation, and network training procedures.