This tutorial introduces the CMA Evolution Strategy (ES), where CMA stands for Covariance Matrix Adaptation. The CMA-ES is a stochastic, or randomized, method for real-parameter (continuous domain) optimization of non-linear, non-convex functions. We try to motivate and derive the algorithm from intuitive concepts and from requirements of non-linear, non-convex search in continuous domain.

翻译：本教程介绍CMA进化策略（ES），其中CMA代表协方差矩阵自适应（Covariance Matrix Adaptation）。CMA-ES是一种基于随机化方法，用于非线性、非凸函数在实参数（连续域）优化中的随机性方法。我们尝试从直观概念以及连续域中非线性、非凸搜索的需求出发，启发并推导该算法。