Natural Evolution Strategy

Natural Evolution Strategies (NES) are a class of gradient-free optimization algorithms that efficiently optimize black-box objective functions by iteratively updating the parameters of a probability distribution, typically a multivariate Gaussian. Current research focuses on extending NES to handle discrete parameter spaces, improving their performance in high-dimensional problems and noisy environments through techniques like covariance matrix adaptation and learning rate adjustments, and applying them to diverse applications such as reinforcement learning, variational inference, and mixed-integer optimization. The ability of NES to handle complex, non-differentiable, or noisy problems makes them a valuable tool across various fields, offering a robust alternative to gradient-based methods where those are unavailable or impractical.