Parsimonious Support Vector Machines Modelling for Set Points in Industrial Processes Based on Genetic Algorithm Optimization

Abstract

An optimization based on genetic algorithms for both feature selection and model tuning is presented to improve the prediction of set points in industrial lines. The objective is the development of an automatic procedure that efficiently generates parsimonious prediction models with higher generalisation capacity. These models can achieve higher accuracy in predictions, maintaining the high quality of products while working with continual changes in the production cycle. The proposed method deals with three strict restrictions: few individuals per population, low number of holds and runs in model validation procedure and a reduced number of maximum generations. To fullfill these restrictions, we propose to include in the optimization the reranking of the individuals by their complexity when no significant difference is found between the values of their fitness functions. The method is applied to develop support vector machines for predicting three temperature set points in the annealing furnace of a continuous hot-dip galvanising line. The results demonstrate the rerank makes more efficiently and easily the process of obtaining parsimonious models without reducing performance.