Feature selection is a important tast to reduce dimensionality in large datasets. Datasets from multivariate calibration problems are a good example lf datasets with a large number of features. In literature, there are several types of techniques to reduce the number of features for this problem, among them, evolutionary algorithms such as genetic algorithms (GAs). They have been successfully used with binary encoding to select features in multivariate calibration. However, as far as we know, there is no work in literature which provides an integer encoding GA in such context. Thus, this paper presents an integer-based GA implementation for feature selection in multivariate calibration models. The results demonstrated that our proposal is able to outperform the outcomes of participants from 2014 IDRC regarding model prediction error as well as number of selected features. In this dataset, the samples correspond to oils from petroleum reservoirs around the world and gas mixtures in the gas phase measured in transmittance. The gain of our proposed implementation in relation to the winner was from 20.9% up to 88.8%.
