Selection genetic programming

J.R. Koza, Genetic Programming One the Programming of Computers by Natural Selection , MIT Press, Cambride, MA, 1992. 

Davis, R. A., Charlton, A. J., Oehlschlager, S., and Wilson, J. C. (2006). Novel feature selection method for genetic programming using metabolomic FI-1 NMR data. Chemom. Intell. Lab. 81, 50-59. 

Koza JR 1992 Genetic Programming. On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge, Massachusetts. 

New methods of variable selection include evolutionary methods based on Darwinian principles including Genetic Algorithms and Genetic Programming... 

Genetic programming [137] is an evolutionary technique which uses the concepts of Darwinian selection to generate and optimise a desired computational function or mathematical expression. It has been comprehensively studied theoretically over the past few years, but applications to real laboratory data as a practical modelling tool are still rather rare. Unlike many simpler modelling methods, GP model variations that require the interaction of several measured nonlinear variables, rather than requiring that these variables be orthogonal. 

Genetic programming, described earlier, picks only certain variables from the model. The rules, which may be in the form of a computer language such as Lisp, or easily interpretable equations, produce a formula from which a result can be calculated (e.g. if (measurement 1 >2.37 and measurement 2<0.53) or measurement 3 > 4.28 then sample is adulterated else sample is clean) [162 — 165]. Rather than being a pre-processing step before statistical analysis, this method combines the variable selection and model formation stages into one. 

In view of the importance of choosing appropriate values for EA variables, it is common for these to be selected through a combination of past experience with similar problems and some testing of the dependence of the quality of solution reached with different population sizes, mutation rates and so on. Since the population size in different methods varies considerably (in a GA it is typically in the range 30-100, while genetic programming (GP) populations may... 

KozaJR (1992) Genetic programming on the programming of computers by means of natural selection. MIT Press, Cambridge... 

Feature Selection Using Multiobjective Genetic Programming. 146... 

Once production of the desired secondary compound is demonstrated in cell cultures, the emphasis can be shifted toward inducing and selecting genetic variants that synthesize increased levels of the compound. However, before one can embark on a program of genetic modification for enhanced production capacity, rapid, but sensitive assay procedures must be developed for the detection of the desired compounds (10). These methods should be geared to handle a large number of samples quickly, but should minimize the amount of tissue required for analysis. 

Soybean breeders and geneticists use many different breeding methods for cultivar and/or germplasm development. Most, if not all, methods have a number of aspects in common. They include the objectives of the breeding/genetics program, selection of parents, type of populations and selection, and inbred line development. 

Koza, J. (1992). Genetic Programming On Programming of Computer by Means of Natural Selection, Cambridge, MA MIT Press. 

Koza, J. R. (1994a) Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4, 87-112. 

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