Genetic algorithms combined with discriminant analysis for key variable identification

Many trouble-shooting problems in process industries are related to key variable identification for classifications. The contribution charts, based on principal component analysis (PCA), can be applied for this purpose. Genetic algorithms (GAs) have been proposed recently for many applications including variable selection for multivariate calibration, molecular modeling, regression analysis, model identification, curve fitting, and classification. In this paper, GAs are incorporated with Fisher discriminant analysis (FDA) for key variable identification. GAs are used as an optimization tool to determine variables that maximize the FDA classification success rate for two given data sets. GA/FDA is a proposed solution for the variable selection problem in discriminant analysis. The Tennessee. Eastman process (TEP) simulator was used to generate the data sets to evaluate the correctness of the key variable selection using GA/FDA, and the T-2 and Q statistic contribution charts. GA/FDA correctly identifies the key variables for the TEP case studies that were tested. For one case study where the correlation changes in two data sets, the contribution charts incorrectly suggest that the operating conditions are similar. On the other hand, GA/FDA not only determines that the operating conditions are different, but also identifies the key variables for the change. For another case study where many key variables are responsible for the changes in the two data sets, the contribution charts only identifies a fraction of the key variables, while GA/ FDA correctly identifies all of the key variables. GA/FDA is a promising technique for key variable identification, as is evidenced in successful applications at The Dow Chemical Company. (C) 2003 Elsevier Ltd. All rights reserved.