Evolving Neural Networks is an innovative approach that combines principles of evolutionary algorithms with neural network design. As deep learning continues to dominate the machine learning landscape, the complexity of designing effective neural architectures has increased. Evolving Neural Networks leverage genetic algorithms to optimize and adapt neural network structures, enabling the discovery of architectures that may outperform traditional designs. This method not only enhances performance in various tasks, such as reinforcement learning, but also addresses challenges like premature convergence. By evolving networks, researchers aim to create more efficient and capable models that can tackle complex problems across diverse applications.