Evolving Neural Networks refers to the innovative approach of using evolutionary algorithms to design and optimize neural network architectures. As deep learning continues to advance, the complexity of creating effective neural networks increases. Traditional methods often struggle with this complexity, leading researchers to explore evolutionary strategies. These strategies mimic natural selection processes, allowing networks to evolve over generations, improving their performance in tasks such as reinforcement learning and image recognition. This approach not only enhances the efficiency of neural networks but also opens new avenues for developing architectures that can adapt to various challenges in machine learning.