Covariance Matrix Adaptation Evolution Strategy

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a powerful stochastic optimization algorithm used for efficiently finding optimal solutions in complex, high-dimensional search spaces where gradient information is unavailable. Current research focuses on improving CMA-ES's performance in noisy environments, handling discrete or mixed-variable problems, and adapting it for large-scale parallel optimization and specific applications like robotics control and adversarial attacks on neural networks. These advancements enhance CMA-ES's applicability to a wider range of challenging optimization problems across diverse scientific and engineering domains, offering significant improvements in efficiency and robustness.