  We consider Evolution Strategies operating only with isotropic Gaussian
mutations on positive quadratic objective functions, and investigate the
covariance matrix when constructed out of selected individuals by truncation.
We prove that the covariance matrix over $(1,\lambda)$-selected decision
vectors becomes proportional to the inverse of the landscape Hessian as the
population-size $\lambda$ increases. This generalizes a previous result that
proved an equivalent phenomenon when sampling was assumed to take place in the
vicinity of the optimum. It further confirms the classical hypothesis that
statistical learning of the landscape is an inherent characteristic of standard
Evolution Strategies, and that this distinguishing capability stems only from
the usage of isotropic Gaussian mutations and rank-based selection. We provide
broad numerical validation for the proven results, and present empirical
evidence for its generalization to $(\mu,\lambda)$-selection.
