loading...
 This Article 
   
 Share 
   
 Bibliographic References 
   
 Add to: 
 
Digg
Furl
Spurl
Blink
Simpy
Google
Del.icio.us
Y!MyWeb
 
 Search 
   
2009 IEEE International Symposium on Parallel&Distributed Processing
Fine grained population diversity analysis for parallel genetic programming
Rome, Italy
May 23-May 29
ISBN: 978-1-4244-3751-1
ASCII Text
x 
 
Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, "Fine grained population diversity analysis for parallel genetic programming," Parallel and Distributed Processing Symposium, International, pp. 1-8, 2009 IEEE International Symposium on Parallel&Distributed Processing, 2009.
 
 
BibTex
x
 
@article{ 10.1109/IPDPS.2009.5161117,
author = {Stephan M. Winkler and Michael Affenzeller and Stefan Wagner},
title = {Fine grained population diversity analysis for parallel genetic programming},
journal ={Parallel and Distributed Processing Symposium, International},
volume = {0},
year = {2009},
isbn = {978-1-4244-3751-1},
pages = {1-8},
doi = {http://doi.ieeecomputersociety.org/10.1109/IPDPS.2009.5161117},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Parallel and Distributed Processing Symposium, International
TI - Fine grained population diversity analysis for parallel genetic programming
SN - 978-1-4244-3751-1
SP1
EP8
A1 - Stephan M. Winkler,
A1 - Michael Affenzeller,
A1 - Stefan Wagner,
PY - 2009
VL - 0
JA - Parallel and Distributed Processing Symposium, International
ER -
 
Stephan M. Winkler, Department for Medical and Bioinformatics, Upper Austria University of Applied Sciences, Hagenberg, Austria
Michael Affenzeller, Department for Software Engineering, Upper Austria University of Applied Sciences, Hagenberg, Austria
Stefan Wagner, Department for Software Engineering, Upper Austria University of Applied Sciences, Hagenberg, Austria
In this paper we describe a formalism for estimating the structural similarity of formulas that are evolved by parallel genetic programming (GP) based identification processes. This similarity measurement can be used for measuring the genetic diversity among GP populations and, in the case of multi-population GP, the genetic diversity among sets of GP populations: The higher the average similarity among solutions becomes, the lower is the genetic diversity. Using this definition of genetic diversity for GP we test several different GP based system identification algorithms for analyzing real world measurements of a BMW Diesel engine as well as medical benchmark data taken from the UCI machine learning repository.
Citation:
Stephan M. Winkler, Michael Affenzeller, Stefan Wagner, "Fine grained population diversity analysis for parallel genetic programming," ipdps, pp.1-8, 2009 IEEE International Symposium on Parallel&Distributed Processing, 2009
Usage of this product signifies your acceptance of the Terms of Use.