Fine grained population diversity analysis for parallel genetic programming

Author

Winkler, Stephan M. ; Affenzeller, Michael ; Wagner, Stefan

Author_Institution

Dept. for Med. & Bioinf., Upper Austria Univ. of Appl. Sci., Hagenberg, Austria

fYear

2009

fDate

23-29 May 2009

Firstpage

1

Lastpage

8

Abstract

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.

Keywords

genetic algorithms; identification; parallel programming; BMW diesel engine; GP population; genetic diversity; multipopulation GP; parallel genetic programming; population diversity; similarity measurement; structural similarity; system identification; Context modeling; Diversity reception; Genetic algorithms; Genetic mutations; Genetic programming; Machine learning; Mathematical model; Predictive models; System identification; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Parallel & Distributed Processing, 2009. IPDPS 2009. IEEE International Symposium on

Conference_Location

Rome

ISSN

1530-2075

Print_ISBN

978-1-4244-3751-1

Electronic_ISBN

1530-2075

Type

conf

DOI

10.1109/IPDPS.2009.5161117

Filename

5161117