| has gloss | eng: CMA-ES stands for Covariance Matrix Adaptation Evolution Strategy. An evolution strategy (ES) is a stochastic, derivative-free numerical optimization method for non-linear or non-convex problems, belonging to the class of evolutionary algorithms and evolutionary computation. The covariance matrix adaptation (CMA) is a method to update the covariance matrix of the multivariate normal search distribution in the evolution strategy. New candidate solutions are sampled according to the search distribution. The covariance matrix describes the pairwise dependencies between the variables in this distribution. Adaptation of the covariance matrix amounts to learning a second order model of the underlying objective function similar to the approximation of the inverse Hessian matrix in the Quasi-Newton method in classical optimization. In contrast to classical methods, only the ranking between candidate solutions is exploited during learning. Neither derivatives nor even the function values itself are required by the method. |
