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- Gaussian_adaptation abstract "Gaussian adaptation (GA) (also referred to as normal or natural adaptation and sometimes abbreviated as 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[xT = (x1, x2, ..., 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*.The outcome of x as a pass sample is determined by a function s(x), 0 < s(x) < q ≤ 1, such that s(x) is the probability that x will be selected as a pass sample. The average probability of finding pass samples (yield) isThen the theorem of GA states:For any s(x) and for any value of P < q, there always exist a Gaussian p. d. f. that is adapted for maximum dispersion. The necessary conditions for a local optimum are m = m* and M proportional to M*. The dual problem is also solved: P is maximized while keeping the dispersion constant (Kjellström, 1991).Proofs of the theorem may be found in the papers by Kjellström, 1970, and Kjellström & Taxén, 1981.Since dispersion is defined as the exponential of entropy/disorder/average information it immediately follows that the theorem is valid also for those concepts. Altogether, this means that Gaussian adaptation may carry out a simultaneous maximisation of yield and average information (without any need for the yield or the average information to be defined as criterion functions).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, m, is then moved to the centre of gravity of the approved (selected) points, m*. 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 (in analogy to simulated annealing, Kirkpatrick 1983). Since 1970 it has been used for both ordinary optimization and yield maximization.".
- Gaussian_adaptation thumbnail Fraktal.gif?width=300.
- Gaussian_adaptation wikiPageID "9210345".
- Gaussian_adaptation wikiPageRevisionID "552857939".
- Gaussian_adaptation coi "March 2009".
- Gaussian_adaptation date "March 2009".
- Gaussian_adaptation expert "Mathematics".
- Gaussian_adaptation hasPhotoCollection Gaussian_adaptation.
- Gaussian_adaptation primarysources "July 2008".
- Gaussian_adaptation refimprove "July 2008".
- Gaussian_adaptation subject Category:Creationism.
- Gaussian_adaptation subject Category:Creativity.
- Gaussian_adaptation subject Category:Evolutionary_algorithms.
- Gaussian_adaptation subject Category:Free_will.
- Gaussian_adaptation type Abstraction100002137.
- Gaussian_adaptation type Act100030358.
- Gaussian_adaptation type Activity100407535.
- Gaussian_adaptation type Algorithm105847438.
- Gaussian_adaptation type Event100029378.
- Gaussian_adaptation type EvolutionaryAlgorithms.
- Gaussian_adaptation type Procedure101023820.
- Gaussian_adaptation type PsychologicalFeature100023100.
- Gaussian_adaptation type Rule105846932.
- Gaussian_adaptation type YagoPermanentlyLocatedEntity.
- Gaussian_adaptation comment "Gaussian adaptation (GA) (also referred to as normal or natural adaptation and sometimes abbreviated as 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[xT = (x1, x2, ..., xn)] are taken from a multivariate Gaussian distribution, N(m, M), having mean m and moment matrix M.".
- Gaussian_adaptation label "Gaussian adaptation".
- Gaussian_adaptation sameAs m.0280dkf.
- Gaussian_adaptation sameAs Q5527832.
- Gaussian_adaptation sameAs Q5527832.
- Gaussian_adaptation sameAs Gaussian_adaptation.
- Gaussian_adaptation wasDerivedFrom Gaussian_adaptation?oldid=552857939.
- Gaussian_adaptation depiction Fraktal.gif.
- Gaussian_adaptation isPrimaryTopicOf Gaussian_adaptation.