Gaussian adaptation (GA), also called normal or natural adaptation (NA) is an evolutionary algorithm designed for the maximization of manufacturing yield due to statistical deviation of component values of signal processing systems. In short, GA is a stochastic adaptive process where a number of samples of an "n"-dimensional vector "x"["x"T = ("x"1, "x"2, ..., "xn")] are taken from a multivariate Gaussian distribution, "N"("m", "M"), having mean "m" and moment matrix "M". The samples are tested for fail or pass. The first- and second-order moments of the Gaussian restricted to the pass samples are "m*" and "M*".