Direct Utilization of MATLAB Genetic Algorithm Toolbox
The weight optimization of BP neural networks represents an unconstrained optimization problem with real-number encoding, making it suitable for direct implementation using MATLAB's genetic algorithm toolbox. The provided code demonstrates nonlinear regression for a system with 19 input variables and 1 output variable. For adaptation to different scenarios, users need only modify the encoding/decoding functions. The implementation requires the GAOT (Genetic Algorithm Optimization Toolbox) with enhancements including optimization algorithm selection, automated parameter tuning, and parallel computation capabilities.