Comparing Genetic Programming with Other Data Mining Techniques on Prediction Models

Abstract

Prediction is one of the most important tasks in the machine learning field. Data scientists employ various learning methods to find the most appropriate and accurate model for each family of applications or dataset. This study compares the symbolic regression utilizing genetic programming (GP), with conventional machine learning techniques. In cases it is required to model an unknown, poorly understood, and/or complicated system. In these cases, we utilize genetic programming to generate a symbolic model without using any pre-known model. In this paper, the GP is studied as a tool for prediction in different types of datasets and conducted experiments to verify the superiority of GP over conventional models in certain conditions and datasets. The accuracy of GP-based regression results are compared with other machine learning techniques, and are found to be more accurate in certain conditions.