A new multiobjective genetic programming approach using compromise distance ranking for automated design of nonlinear system design

This paper presents a new multiobjective genetic programming (MOGP) approach, to realize an all-in-one automatic nonlinear system design (NSD). The nonlinear system design is here modeled as a multiobjective optimization problem (MOP) to solve parameter estimation, structure optimization and feature selection simultaneously. The novel MOGP method is then proposed to rank individuals according to the ‘compromise distance’ between them, which has the benefit of combining decision making for NSD with the optimization process to get the final compromise solution in a single process. The effectiveness of the proposed learning approach for nonlinear system design is verified through experiments on the classical nonlinear autoregressive with extra inputs (NARX) system by comparison with classical aggregating method and a Paretobased method for MOP. Finally, experimental results demonstrate the proposed approach is available to explore the unknown structure of nonlinear systems as well as the features and parameters with high accuracy and efficiency.