An improved Gene Expression Programming approach for symbolic regression problems

Gene Expression Programming (GEP) is a powerful evolutionary method for knowledge discovery and model learning. Based on the basic GEP algorithm, this paper proposes an improved algorithm named S_GEP, which is especially suitable for dealing with symbolic regression problems. The major advantages for this S_GEP method include: (1) A new method for evaluating individual without expression tree; (2) a corresponding expression tree construction schema for the new evaluating individual method if required by some special complex problems; and (3) a new approach for manipulating numeric constants so as to improve the convergence. A thorough comparative study between our proposed S_GEP method with the primitive GEP, as well as other methods are included in this paper. The comparative results show that the proposed S_GEP method can significantly improve the GEP performance. Several well-studied benchmark test cases and real-world test cases demonstrate the efficiency and capability of our proposed S_GEP for symbolic regression problems.