Optimization of BP Neural Network Weights and Thresholds Using Genetic Algorithm

By employing genetic algorithms to optimize the weights and thresholds of a Backpropagation (BP) neural network, repeated training sessions can significantly improve model accuracy and performance. This methodology enhances target value prediction capabilities and demonstrates practical utility across various real-world applications. Below is a detailed example illustrating the implementation process.

For instance, consider predicting housing prices in a specific region. We can collect features such as property size, number of rooms, and geographical location as input data. The genetic algorithm implementation typically involves: - Initialization: Generating a population of random weight and threshold combinations - Fitness evaluation: Measuring network performance using mean squared error - Selection: Applying roulette wheel or tournament selection for parent chromosomes - Crossover: Using single-point or uniform crossover to create offspring - Mutation: Introducing small random changes to maintain diversity

The optimized weights and thresholds are then fed into the BP neural network for training. The training process involves: - Forward propagation: Calculating network outputs using sigmoid or ReLU activation functions - Error computation: Comparing predictions with actual values - Backpropagation: Adjusting weights using gradient descent with momentum - Iteration: Repeating until convergence criteria are met

After multiple training epochs, a fully trained model emerges capable of predicting housing prices based on given features. Key advantages of this hybrid approach include: - Enhanced convergence: Genetic algorithms help escape local minima - Adaptive learning: Automatic parameter tuning improves generalization - Versatility: Applicable to diverse regression and classification problems - Data efficiency: Maximizes predictive accuracy while minimizing overfitting

Through genetic algorithm optimization of BP neural networks, we can better leverage dataset characteristics and achieve more accurate predictive outcomes. The implementation typically involves Python libraries like TensorFlow/PyTorch for neural network operations and DEAP for genetic algorithm components.