Paper Info
Title
On the Applicability of Random and the Best Solution Driven Metaheuristics for Analytic Programming and Time Series Regression.
Abstract
This paper provides a closer insight into applicability and performance of the hybridization of symbolic regression open framework, which is Analytical Programming (AP) and Differential Evolution (DE) algorithm in the task of time series regression. AP can be considered as a robust open framework for symbolic regression thanks to its usability in any programming language with arbitrary driving metaheuristic. The motivation behind this research is to explore and investigate the applicability and differences in performance of AP driven by basic canonical entirely random or best solution driven mutation strategies of DE. An experiment with four case studies has been carried out here with the several time series consisting of GBP/USD exchange rate. The differences between regression/prediction models synthesized using AP as a direct consequence of different DE strategies performances are statistically compared and briefly discussed in conclusion section of this paper.
Year
DOI
Venue
2018
10.1007/978-3-319-91189-2_48
ARTIFICIAL INTELLIGENCE AND ALGORITHMS IN INTELLIGENT SYSTEMS
Keywords
Field
DocType
Analytic programming,Differential Evolution,Time series regression
Time series,Regression,Computer science,Usability,Theoretical computer science,Differential evolution,Predictive modelling,Symbolic regression,Exchange rate,Metaheuristic
Conference
Volume
ISSN
Citations 
764
2194-5357
0
PageRank 
References 
Authors
0.34
17
5
Name
Order
Citations
PageRank
Roman Senkerik137574.92
Adam Viktorin22916.76
Michal Pluhacek321747.34
Tomas Kadavy42020.97
Zuzana Kominkova Oplatkova58417.68