Abstract
Genetic regression (GR) is an application of genetic algorithms to the problem of producing optimal calibration models by wavelength selection. GR has been shown to provide excellent calibration models under many conditions that typically result in poor calibration models with the use of other multivariate techniques. In this study, GR is applied to the calibration of the components of a ternary mixture with the use of near-infrared spectroscopic data. To determine how close GR comes to the true global optimum, a random search of the possible solutions was performed and the distribution of the solutions' predictive abilities determined. Through this study it has been determined that GR is capable of searching through extremely large search spaces and eliminating over 99.9999% of the unsuitable solutions in a matter of minutes. GR is also capable of finding multiple solutions of similar quality, something not available in many other calibration techniques. Comparison with results from partial least-squares (PLS) is also included.
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