Direct Utilization of MATLAB Genetic Algorithm Toolbox

Since BP neural network weight optimization constitutes an unconstrained optimization problem requiring real-number encoding, we can directly employ MATLAB's genetic algorithm toolbox for solution. The following code is designed for nonlinear regression involving 19 input variables and 1 output variable. Adaptation to other scenarios requires only modifications to the encoding/decoding functions, while the program necessitates the GAOT toolbox. To enhance program versatility, we propose several functional extensions: - Optimization Algorithm Selection: Beyond genetic algorithms, alternative optimization techniques like Particle Swarm Optimization (PSO) or Simulated Annealing (SA) can be implemented. This allows algorithm selection based on specific problem characteristics, where different algorithms may offer distinct advantages in convergence speed or solution quality. - Automated Parameter Tuning: Implementing automatic hyperparameter optimization using metaheuristic algorithms can search for optimal parameter combinations (e.g., population size, mutation rates) to enhance algorithm performance. This can be achieved through nested optimization loops or Bayesian optimization techniques. - Parallel Computing Capabilities: Integration of parallel computing functionality utilizing multi-core processors or distributed computing environments can significantly accelerate optimization processes. MATLAB's Parallel Computing Toolbox can be leveraged through parfor loops or distributed arrays for fitness function evaluations. These enhancements are implemented through modular function design, where core components include chromosome encoding/decoding functions, fitness evaluation routines, and algorithm selection switches. The genetic algorithm implementation typically involves initialization, selection, crossover, and mutation operations, with key functions including gaopt for optimization setup and fitnessfcn for objective function definition. We hope these suggestions prove beneficial for your implementation.