Paper Info
Title
Performance Comparison Of Differential Evolution Driving Analytic Programming For Regression
Abstract
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.
Year
DOI
Venue
2017
10.1109/SSCI.2017.8285430
2017 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI)
Keywords
Field
DocType
Analytic Programming, Differential Evolution, SHADE, Time series regression
Time series,Swarm behaviour,Regression,Regression analysis,Computer science,Theoretical computer science,Differential evolution,Analytic programming,Symbolic regression,Exchange rate
Conference
Citations 
PageRank 
References 
1
0.35
0
Authors
5
Name
Order
Citations
PageRank
Roman Senkerik137574.92
Adam Viktorin258.23
Michal Pluhacek321747.34
Tomas Kadavy42020.97
Zuzana Kominkova Oplatkova58417.68