Deep Neural Networks (DNNs) have demonstrated remarkable success across various domains; however, their performance heavily depends on effective architecture design, parameter tuning, and optimization strategies. Traditional gradient-based optimization methods often suffer from challenges such as local minima, vanishing gradients, and computational inefficiency. Evolutionary Machine Learning (EML) offers a robust alternative by employing population-based search strategies inspired by natural evolution, such as genetic algorithms, particle swarm optimization, and differential evolution. This paper explores the integration of evolutionary approaches to optimize neural network architectures, weights, and hyperparameters. Experimental results demonstrate that evolutionary optimization enhances convergence speed, generalization capability, and robustness of DNNs, making it a promising direction for real-world applications.
Deep Neural Networks, Evolutionary Machine Learning, Optimization, Genetic Algorithms, Particle Swarm Optimization, Differential Evolution, Hyperparameter Tuning, Neural Architecture Search