Developed BP Algorithm Enhanced with Genetic Algorithm

The genetic algorithm-enhanced BP algorithm integrates two distinct optimization methods to improve neural network training efficiency and prediction accuracy. The BP algorithm (Backpropagation) is a classic neural network training method that adjusts weights and thresholds through gradient descent, but it often converges to local optima. In contrast, the Genetic Algorithm (GA) is a global optimization approach based on natural selection and genetic mechanisms, capable of searching for optimal solutions within complex solution spaces.

In this MATLAB implementation, the genetic algorithm primarily optimizes the initial weights and thresholds of the BP neural network to mitigate training instability caused by random initialization. Through operations including encoding, selection, crossover, and mutation, the GA explores superior initial parameters across a wide search space, thereby enhancing the convergence speed and generalization capability of the BP algorithm. The key coding implementation utilizes MATLAB's global optimization toolbox functions such as `ga` for genetic algorithm operations and custom fitness functions to evaluate network performance.

The program's execution logic can be summarized in the following steps: Encoding and Population Initialization: Neural network weights and thresholds are encoded into chromosomes, with random generation of the initial population using MATLAB's population initialization functions. Fitness Evaluation: The training error of the BP network serves as the fitness function, calculated through forward propagation and error computation to assess individual performance. Selection, Crossover, and Mutation: Implements roulette wheel selection, single-point crossover, and mutation operations using genetic algorithm operators to evolve the population. Optimal Individual Decoding: The best chromosome obtained from the GA is decoded back into initial weights and thresholds for the neural network. BP Network Training: The refined initial parameters are used for BP training through the `train` function, improving convergence speed while reducing local optimum risks.

This hybrid approach is particularly suitable for modeling complex nonlinear problems in domains such as financial forecasting, industrial control, and pattern recognition, significantly enhancing the robustness and prediction accuracy of neural networks through intelligent parameter initialization.