BP Genetic Hybrid Algorithm

This paper introduces a hybrid algorithm integrating genetic algorithms and backpropagation (BP) neural networks. This combined approach leverages the strengths of both algorithms to enhance prediction accuracy and computational efficiency. Genetic algorithms are optimization techniques inspired by natural selection and evolutionary principles, particularly effective for global optimization and near-optimal solution discovery. Backpropagation is a widely-used artificial neural network algorithm primarily applied to regression and classification problems through gradient-based weight adjustments. The hybrid algorithm synergistically combines genetic algorithms' global exploration capabilities with BP's local refinement precision, enabling superior performance in complex problem-solving scenarios. Key implementation aspects include using genetic operators (selection, crossover, mutation) for initial weight optimization followed by BP fine-tuning, where chromosome encoding typically represents neural network weights and fitness evaluation employs mean squared error metrics.