Enhanced Differential Evolution Algorithm with Neural Network Optimization

This project involves implementing an enhanced differential evolution algorithm to optimize neural networks. Differential evolution is a population-based optimization algorithm that mimics biological evolution processes including mutation, crossover, and selection mechanisms to find optimal solutions. When developing the improved version, new mutation strategies such as DE/rand-to-best/1 or adaptive parameter control can be implemented through code structures like: def mutation_strategy(population, F, best_individual): # Implementation of advanced mutation operators return mutant_vectors The crossover mechanism can be enhanced using binomial or exponential crossover methods with dynamic crossover rate (CR) adaptation. Selection rules can incorporate elitism or tournament selection to improve convergence speed and search capability. For neural network optimization, various activation functions (ReLU, sigmoid, tanh) can be tested through modular code implementation: class ActivationFunction: def relu(x): return np.maximum(0, x) def sigmoid(x): return 1/(1+np.exp(-x)) Additionally, network architectures including layer configurations and neuron counts can be optimized using the algorithm's parameter encoding scheme. Learning algorithms can incorporate adaptive learning rates or momentum techniques. By integrating the enhanced differential evolution algorithm with neural network optimization techniques, more accurate and efficient problem-solving and model training can be achieved through systematic parameter tuning and architecture search. The implementation would typically involve fitness evaluation functions that measure neural network performance metrics like accuracy or loss.