- Published 2009 in Neural Computing and Applications

A novel genetic programming (GP) algorithm called parsimonious genetic programming (PGP) for complex process intelligent modeling was proposed. First, the method uses traditional GP to generate nonlinear input–output model sets that are represented in a binary tree structure according to special decomposition method. Then, it applies orthogonal least squares algorithm (OLS) to estimate the contribution of the branches, which refers to basic function term that cannot be decomposed anymore, to the accuracy of the model, so as to eliminate complex redundant subtrees and enhance convergence speed. Finally, it obtains simple, reliable and exact linear in parameters nonlinear model via GP evolution. Simulations validate that the proposed method can generate more robust and interpretable models, which is obvious and easy for realization in real applications. For the proposed algorithm, the whole modeling process is fully automatic, which is a rather promising method for complex process intelligent modeling.

@article{Wei2009ParsimoniousGP,
title={Parsimonious genetic programming for complex process intelligent modeling: algorithm and applications},
author={Xunkai Wei and Yinghong Li and Yue Feng},
journal={Neural Computing and Applications},
year={2009},
volume={19},
pages={329-335}
}