Gaussian Adaptation (GA), is an algorithm that adapts a Gaussian distribution to a feasible region such that its entropy is maximized while keeping the hitting probability constant. Typical areas of application are (1) minimization of functions that may be non-differentiable, (2) maximization of manufacturing yield and (3) discrete optimization. In this contribution it will be shown that GA is a simple, efficient global optimizer. Despite its simplicity, GA may be seen as a second order approximation of the natural evolution of populations