CMA E

Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is a powerful stochastic optimization algorithm used for finding optimal solutions in complex, high-dimensional search spaces, particularly in black-box scenarios where the objective function is unknown. Current research focuses on improving CMA-ES's efficiency and robustness, particularly for noisy, multi-modal, and mixed-integer problems, through techniques like adaptive re-evaluation, low-discrepancy sampling, and novel learning rate and margin adaptations. These advancements enhance CMA-ES's applicability across diverse fields, including engineering design, machine learning, and scientific simulations, by providing more efficient and reliable optimization solutions.