Performance comparison of differential evolution driving analytic programming for regression

This research deals with the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series regression. This paper provides a closer insight into performance comparisons of connection between AP and different strategies of DE. AP can be considered as a powerful open framework for symbolic regression thanks to its applicability in any programming language with arbitrary driving evolutionary/swarm based algorithm. Thus, the motivation behind this research is to explore and investigate the differences in performance of AP driven by basic canonical strategies of DE as well as by the state of the art strategies, which is Success-History based Adaptive Differential Evolution (SHADE) and its variant L-SHADE. Simple experiments have been carried out here with the four different time series of EUR/USD exchange rate. DE performance analysis, as well as the differences between regression models synthesized using AP as direct consequences of different DE strategies performances, are both discussed within conclusion section.