As. Barros et Dn. Rutledge, GENETIC ALGORITHM APPLIED TO THE SELECTION OF PRINCIPAL COMPONENTS, Chemometrics and intelligent laboratory systems, 40(1), 1998, pp. 65-81
The application of a genetic algorithm (GA) to the selection of princi
pal components (PCs) is proposed as an efficient method to determine t
he optimal multivariate regression model. This stochastic method was c
ompared with other deterministic methods such as: exhaustive search (h
ere taken as a validation procedure), forward and backward-stepwise va
riable selection and correlation principal components regression (CPCR
). It is shown that for the range of data sets used, the GA gives the
same result as the those obtained by an exhaustive search and by CPCR
whereas the stepwise procedures do not. These results also show that i
n order to build optimal predictive models using principal components
regression (PCR) one needs to select the best subset of PCs rather tha
n simply use those with the highest eigenvalues. (C) 1998 Elsevier Sci
ence B.V. All rights reserved.