Model Evolution

Model evolution encompasses the automated design and improvement of machine learning models, aiming to surpass traditional manual development through iterative optimization. Current research focuses on leveraging large language models to guide evolutionary algorithms, incorporating complementary learning systems for enhanced data adaptation, and employing neuroevolution for efficient architecture search across various model types, including deep learning architectures for tasks like anomaly detection and image generation. These advancements promise faster, more efficient model development, leading to improved performance and reduced development costs across diverse applications.