Automatic discovery of the regression model by the means of grammatical and differential evolution

Abstract

In the contribution, there is discussed the usage of the method based on the grammatical and differential evolution for the automatic discovery of regression models for discrete datasets. The combination of these two methods enables the process to find the precise structure of the mathematical model and values for the model constants separately. The used method is described and tested on the selected regression examples. The results are reported and the obtained mathematical models are presented. The advantages of the selected approach are described and compared to the classical methods.