NeuroEvolution Algorithm

Neuroevolution algorithms optimize neural network architectures and weights using evolutionary principles, aiming to automate the design and training of effective models for various tasks. Current research emphasizes improving efficiency through surrogate models and hardware acceleration (e.g., using JAX and TPUs/GPUs), expanding applicability to diverse domains like anomaly detection and physics-informed neural networks, and exploring novel algorithms such as those based on linear genetic programming. This approach offers a powerful alternative to traditional gradient-based methods, particularly for complex problems where finding global optima is crucial, impacting fields ranging from robotics to scientific modeling.