Enhanced Genetic Optimization Algorithm for Addressing Local Minima Issues in BP Neural Networks

This paper provides MATLAB source code that implements an improved genetic optimization algorithm to address local minima issues in Backpropagation (BP) neural networks. The code incorporates enhanced genetic operators including elite selection strategy, adaptive mutation rates, and multi-point crossover operations to optimize neural network training. The algorithm effectively prevents the network from getting trapped in local minima by maintaining population diversity and implementing fitness-based selection. Key functions include population initialization with constrained weights, fitness evaluation using mean squared error, and adaptive genetic operations that dynamically adjust based on convergence progress. This approach significantly improves neural network training efficiency, enables better global optimum discovery, and enhances overall network performance and accuracy across various applications. The implementation includes modular functions for genetic operations, neural network forward/backward propagation, and convergence monitoring with detailed comments for customization.