Skip to Main Content
Gene expression programming (GEP) is a kind of heuristic method based on evolutionary computation theory. Basic GEP method has been proved to be powerful in symbolic regression and other data mining as well as machine learning tasks. However, GEP's potential for neural network learning has not been well studied. In this paper, we prove that GEP neural network (GEPNN) is not able to solve high order regression problems. Based on our proof, we propose an extended method for evolving neural network with GEP. The extended GEPNN is used in various kinds of function finding problems. Results on multiple leaning methods show the effectiveness of our method.