Paper Info
Title
Improving the parsimony of regression models for an enhanced genetic programming process
Abstract
This research is focused on reducing the average size of the solutions generated by an enhanced GP process without affecting the high predictive accuracy the method exhibits when being applied on a complex, industry proposed, regression problem. As such, the effects the GP enhancements have on bloat have been studied and, finally, a bloat control system based on dynamic depth limiting (DDL) and iterated tournament pruning (ITP) was designed. The resulting bloat control system is able to improve by ≃40% the average GP solution parsimony without impacting average solution accuracy.
Year
DOI
Venue
2011
10.1007/978-3-642-27549-4_34
EUROCAST (1)
Keywords
Field
DocType
average gp solution parsimony,high predictive accuracy,gp enhancement,iterated tournament pruning,average solution accuracy,average size,regression problem,bloat control system,regression model,enhanced gp process,enhanced genetic programming process,dynamic depth,genetic programming
Tournament,Computer science,Regression analysis,Genetic programming,Artificial intelligence,Control system,Symbolic regression,Iterated function,Machine learning,Limiting,Pruning
Conference
Volume
ISSN
Citations 
6927
0302-9743
0
PageRank 
References 
Authors
0.34
7
5
Name
Order
Citations
PageRank
Alexandru-Ciprian Zăvoianu100.34
Gabriel Kronberger219225.40
Michael Kommenda39715.58
Daniela Zaharie439336.91
Michael Affenzeller533962.47