Random correlation in variable selection for multivariate calibration with a genetic algorithm

The importance of the validation step in multiple linear regression of near-infrared spectroscopic data, after selection of wavelengths by a genetic algorithm, is investigated with the use of random variables. It is shown that in spite of a careful validation procedure, the GA can still select irrelevant variables. The effect is greatly reduced by applying a forward selection in the subsets selected by the genetic algorithm.