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:本教程引入了CMA Evolution 战略(ES), CMA 战略代表了共性矩阵的适应。 CMA-ES 是一种对非线性、非凝固功能进行实际参数(连续域)优化的随机或随机的方法。 我们试图从直观概念和连续域的非线性、非凝固搜索要求中激励和得出算法。</s>