Variable selection for multivariate calibration using a genetic algorithm: prediction of additive concentrations in polymer films from Fourier transform-infrared spectral data

By R. Leardi, M. B. Seasholtz, R. Pell. 2002

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Variable selection in regression—a tutorial (2010, methodology): “[12] made independent GAs using a large window size and removed the windows of variables that were chosen the least.”
Visible/near infrared-partial least-squares analysis of Brix in sugar cane juice: A test field for variable selection methods (2010, background): “Genetic algorithms (GA) [23–27], particle swarm optimization (PSO) [28], simulated annealing [29] and ant colony optimization (ACO) [30] are pertinent examples.”
Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions (2004, methodology): “This algorithm has already been described in References [2,3] and the reader is referred to these papers for more details.”
A review of variable selection methods in Partial Least Squares Regression (2012, unknown): “[36,37].”