Genetic algorithms applied to the selection of factors in principal component regression

Simultaneous spectrophotometric determination of nitroanilines using genetic-algorithm-based wavelength selection in principal component-artificial neural network

“…A drawback of the techniques of feature selection when applied to spectral data is that usually the selected features (wavelengths) are scattered throughout the spectrum. It has already been shown that genetic algorithms (GAs) (Arcos et al, 1997;Depczynski et al, 2000;Lucasius and Kateman, 1993;Hibbert, 1993) can be successfully used as a feature selection technique (Leardi et al, 1992(Leardi et al, , 1998(Leardi et al, , 2002Leardi, 1994Leardi, , 1996Leardi, , 2000Leardi, , 2007. Leardi and Gonzalez (1998) demonstrated that GAs, after suitable modifications, produce more interpretable results, since the selected wavelengths are less dispersed than with other methods.…”

Quantitative structure-retention relationships analysis of retention index of essential oils

“…A gene was given the value of one, if its corresponding descriptor was included in the subset; otherwise, it was given the value of zero. 22 The GA performs its optimization by variation and selection via the evaluation of the 23 The root-mean-square errors of calibration (RMSEC) and prediction (RMSEP) were calculated and the fitness function was calculated by Equation 1.…”

Application of GA-PLS and GA-KPLS calculations for the prediction of the retention indices of essential oils

“…The fitness function was proposed by Depczynski et al. 23 The root-mean-square errors of calibration (RMSEC) and prediction (RMSEP) were calculated and the fitness function was calculated by Equation 1…”