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Gaussian adaptation is designed for the maximization of manufacturing yield due to statistical deviation of component values of signal processing systems. The process uses the theorem of Gaussian adaptation stating that: ''if the centre of gravity of a high-dimensional Gaussian distribution coincides with the centre of gravity of the part of the distribution belonging to some region of acceptability in a state of selective equilibrium, then the hitting probability on the region is maximal.''

'''Gaussian adaptation''' is designed for the maximization of manufacturing yield due to statistical deviation of component values of signal processing systems. The process uses the theorem of Gaussian adaptation stating that: ''if the centre of gravity of a high-dimensional Gaussian distribution coincides with the centre of gravity of the part of the distribution belonging to some region of acceptability in a state of selective equilibrium, then the hitting probability on the region is maximal.''

The theorem is valid for all regions of acceptability and all Gaussian distributions. It may be used by cyclic repetition of random variation and selection (like the natural evolution). In every cycle a sufficiently large number of Gaussian distributed points are sampled and tested for membership in the region of acceptability. The centre of gravity of the Gaussian is then moved to the centre of gravity of the approved points. Thus, the process converges to a state of equilibrium fulfilling the theorem. A solution is always approximate because the centre of gravity is always determined for a limited number of points.

The theorem is valid for all regions of acceptability and all Gaussian distributions. It may be used by cyclic repetition of random variation and selection (like the natural evolution). In every cycle a sufficiently large number of Gaussian distributed points are sampled and tested for membership in the region of acceptability. The centre of gravity of the Gaussian is then moved to the centre of gravity of the approved points. Thus, the process converges to a state of equilibrium fulfilling the theorem. A solution is always approximate because the centre of gravity is always determined for a limited number of points.

It was used for the first time in 1969 as a pure optimization algorithm making the regions of acceptability smaller and smaller. Since 1970 it has been used for both ordinary optimization and manufacturing yield maximization.

It was used for the first time in 1969 as a pure optimization algorithm making the regions of acceptability smaller and smaller. Since 1970 it has been used for both ordinary optimization and manufacturing yield maximization.

== Gaussian adaptation as a model of evolution ==

== Gaussian adaptation as a model of evolution ==