Enhanced Genetic Operators Design for Waveband Selection in Multivariate Calibration by NIR Spectroscopy

Nowadays the techniques employed in data acquisition provide huge amounts of data. Some parts of the information are related to the others, making desirable a way to reduce the dimensionality, loosing as less information as possible, in order to decrease computational times and complexity when applying any ensuing Data Mining technique. Genetic Algorithms (GA) offer the possibility of selecting which variables contain the most relevant information to represent all the original ones. The traditional genetic operators seem to be too general, leading to results that could be improved by means of designed genetic operators that employ some available problem specific information. Especially, when dealing with calibration by means of NIR spectral data, which use to contain thousands of variables, it is known that not isolated wavelengths but wavebands allow a more robust model design. This aspect should be taken into account when crossing individuals. We propose three crossover operators specifically designed for calibration with NIR spectral data, based on a pseudo-random 2-points crossover where the first point is randomly chosen and the selection of the second point is guided by problem specific information, and we compare their performance against state of the art operators. Our benchmark consists of two real world high dimensional data sets, corresponding to polyetheracrylat (PEA), where hydroxyl number, viscosity and acidity are on-line monitored; and melamine resin production, where the chilling point is considered in order to regulate the condensation. We show that designed operators promote wavebands selection, achieve better good quality solutions, and converge faster and smoother to them than S-o-A operators.