The recent developments and comparative analysis of neural network and evolutionary algorithms for solving symbolic regression

Abstract

Symbolic regression (SR) is one of the research fields in data mining, how to use scientific and appropriate methods to study SR is a difficult problem. The traditional methods used in SR mainly focus on the models such as genetic programming (GP), the article applies the gene expression programming (GEP) and neural network (NN) to this field, in order to correctly compare the advantages and disadvantages of the three methods, some relevant works have been done. This paper first briefly introduces the NN and evolutionary algorithms including GP and GEP, their design steps and recent developments, and applies these algorithms to SR, then uses the algorithms to solve SR and makes comparison analysis, and draws some conclusions in the experiment condition: the performance of NN and evolutionary algorithms change dramatically for solving this problem; GP and GEP fluctuate greatly compared with NN, the used time is also less, and NN shows better stability and result.