Mind Evolutionary Algorithm Optimized BP Neural Network

Application Background Developed by Sun Chengyi et al. in 1998, the Mind Evolutionary Algorithm (MEA) serves as an effective optimization technique. This chapter details MEA's fundamental concepts and implements the algorithm in MATLAB through a nonlinear function fitting case study. Key Technologies 1. Training/Test Set Generation: Creating datasets using MATLAB's rand() and linspace() functions with proper data partitioning 2. Initial Population Initialization: Implementing population initialization with bounds checking using unifrnd() function 3. Subpopulation Convergence Operation: Performing crossover operations with tournament selection and simulated binary crossover (SBX) 4. Subpopulation Dissimilation Operation: Applying mutation operations using polynomial mutation with adaptive mutation rates 5. Optimal Individual Analysis: Implementing fitness evaluation and elite preservation techniques 6. BP Neural Network Training: Configuring network architecture with newff() and optimizing weights using MEA-based training 7. Simulation Testing and Result Analysis: Conducting performance evaluation with MSE metrics and convergence curve plotting