Reducing overfitting in manufacturing process modeling using a backward elimination based genetic programming

Abstract

Genetic programming (GP) has demonstrated as an effective approach in polynomial modeling of manufacturing processes. However, polynomial models with redundant terms generated by GP may depict overfitting, while the developed models have good accuracy on trained data sets but relatively poor accuracy on testing data sets. In the literature, approaches of avoiding overfitting in GP are handled by limiting the number of terms in polynomial models. However, those approaches cannot guarantee terms in polynomial models produced by GP are statistically significant to manufacturing processes. In this paper, a statistical method, backward elimination (BE), is proposed to incorporate with GP, in order to eliminate insignificant terms in polynomial models. The performance of the proposed GP has been evaluated by modeling three real-world manufacturing processes, epoxy dispenser for electronic packaging, solder paste dispenser for electronic manufacturing, and punch press system for leadframe downset in IC packaging. Empirical results show that insignificant terms in the polynomial models can be eliminated by the proposed GP and also the polynomial models generated by the proposed GP can achieve results with better predictions than the other commonly used existent methods, which are commonly used in GP for avoiding overfitting in polynomial modeling.