The optimization is initialized with the mean vector m and the standard deviation vector σcma. For each new generation, λ offspring individuals are sampled with a normal distribution:10\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${{\bf{x}}}_{{\bf{i}}}={\bf{m}}+\sigma N\mathrm{(0},{\bf{C}}),{\bf{for}}\,{\bf{i}}=1,\cdots ,\lambda $$\end{document}xi=m+σN(0,C),fori=1,⋯,λThe mean vector m represents the favourite solution from last generation. The covariance matrix C determines the shape of distribution ellipsoid. This covariance matrix is updated so that to increase the likelihood of successful individuals.