GSP: an automatic programming technique with gravitational search algorithm

Abstract

Recently, automatic programming approaches have attracted great deal of interest aiming to utilize search techniques to find out optimal programs in various problems. Genetic programming is the most commonly explored automatic programming technique which uses genetic algorithm to evolve and discover programs with the tree structure. Herein, we focus on a new gravitational search algorithm (GSA)-based technique to create computer programs, automatically. This method is called gravitational search programming (GSP). Using GSA, the approach of generating the tree structure and insertion of internal nodes has been explained in detail. The GSP has been employed to the symbolic regression (SR) and the problem of feature construction (FC) that are widely used as a mathematical expression fitting to a given set of data points, and a data preprocessing technique for classification, respectively. The proficiency of the proposed algorithm has been evaluated and compared with the well-known automatic programming algorithms as well as C4.5 decision tree classifier. The results have been obtained over ten typical functions and 13 diverse datasets. The obtained results prove the effectiveness of the proposed method in achieving improved accuracy values in comparison to those of competing algorithms.