Paper Info
Title
Application of symbolic regression on blast furnace and temper mill datasets
Abstract
This work concentrates on three different modifications of a genetic programming system for symbolic regression analysis. The coefficient of correlation R2 is used as fitness function instead of the mean squared error and offspring selection is used to ensure a steady improvement of the achieved solutions. Additionally, as the fitness evaluation consumes most of the execution time, the generated solutions are only evaluated on parts of the training data to speed up the whole algorithm. These three algorithmic adaptations are incorporated in the symbolic regression algorithm and their impact is tested on two real world datasets describing a blast furnace and a temper mill process. The effect on the achieved solution quality as well as on the produced models are compared to results generated by a symbolic regression algorithm without the mentioned modifications and the benefits are highlighted.
Year
DOI
Venue
2011
10.1007/978-3-642-27549-4_51
EUROCAST (1)
Keywords
Field
DocType
correlation r2,symbolic regression algorithm,different modification,execution time,temper mill datasets,algorithmic adaptation,fitness function,fitness evaluation,blast furnace,symbolic regression analysis,whole algorithm,genetic programming
Mill,Regression,Computer science,Blast furnace,Mean squared error,Fitness function,Genetic programming,Artificial intelligence,Symbolic regression,Machine learning,Speedup
Conference
Volume
ISSN
Citations 
6927
Computer Aided Systems Theory - EUROCAST 2011, Lecture Notes in Computer Science Volume 6927, 2012, pp 400-407
3
PageRank 
References 
Authors
1.07
3
7
Name
Order
Citations
PageRank
Michael Kommenda19715.58
Gabriel Kronberger219225.40
Christoph Feilmayr331.41
Leonhard Schickmair431.07
Michael Affenzeller533962.47
Stephan M. Winkler614022.90
Stefan Wagner717227.06