Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions

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Stacked partial least squares regression analysis for spectral calibration and prediction (2009, methodology): “Other methods rely on genetic algorithms [7,8] to optimize and select subsets or intervals of the spectrum.”
Variable selection in regression—a tutorial (2010, methodology): “An example of the applicability of biPLS is as input in GA where it can be an advantage to remove non-relevant spectral parts prior to the data analysis [13].”
Variable Selection in Visible and Near-Infrared Spectral Analysis for Noninvasive Determination of Soluble Solids Content of ‘Ya’ Pear (2014, methodology): “…for variable selection include interval PLS (iPLS) (NØgaard et al. 2000), synergy interval PLS (SiPLS) (Wang et al. 2012), backward iPLS (BiPLS) (Leardi and Nørgaard 2004), moving window PLS (MWPLS) (Jiang et al. 2002), genetic algorithms (GA) (Delphine et al. 1995), x-loadingweights (Liu et…”
A review of variable selection methods in Partial Least Squares Regression (2012, methodology): “This has been used for wavelength selection [61].”