Paper Info
Title
Differential Evolution Driven Analytic Programming For Prediction
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 prediction. This paper provides a closer insight into applicability and performance of connection between AP and different strategies of DE. AP can be considered as 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 strategy, which is Success-History based Adaptive Differential Evolution (SHADE). Simple experiment has been carried out here with the time series consisting of 300 data-points of GBP/USD exchange rate, where the first 2/3 of data were used for regression process and the last 1/3 of the data were used as a verification for prediction process. The differences between regression/prediction models synthesized by means of AP as a direct consequences of different DE strategies performances are briefly discussed within conclusion section of this paper.
Year
DOI
Venue
2017
10.1007/978-3-319-59060-8_61
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, ICAISC 2017, PT II
Keywords
Field
DocType
Analytic programming, Differential evolution, SHADE, Time series prediction
Time series,Regression,Swarm behaviour,Computer science,Open framework,Differential evolution,Artificial intelligence,Analytic programming,Predictive modelling,Symbolic regression,Machine learning
Conference
Volume
ISSN
Citations 
10246
0302-9743
1
PageRank 
References 
Authors
0.35
14
5
Name
Order
Citations
PageRank
Roman Senkerik137574.92
Adam Viktorin258.23
Michal Pluhacek321747.34
Tomas Kadavy42020.97
Ivan Zelinka545182.16