Publication detail

Coevolution in Cartesian Genetic Programming

DRAHOŠOVÁ, M. SEKANINA, L.

Original Title

Coevolution in Cartesian Genetic Programming

Type

article in a collection out of WoS and Scopus

Language

English

Original Abstract

Cartesian genetic programming (CGP) is a branch of genetic programming which has been utilized in various applications. This paper proposes to introduce coevolution to CGP in order to accelerate the task of symbolic regression. In particular, fitness predictors which are small subsets of the training set are coevolved with CGP programs. It is shown using five symbolic regression problems that the (median) execution time can be reduced 2-5 times in comparison with the standard CGP.

Keywords

Cartesian genetic programming, coevolution, fitness modeling, fitness predictors, symbolic regression.

Authors

DRAHOŠOVÁ, M.; SEKANINA, L.

RIV year

2012

Released

23. 3. 2012

Publisher

Springer Verlag

Location

Heidelberg

ISBN

978-3-642-29138-8

Book

Proc. of the 15th European Conference on Genetic Programming

Edition

Lecture Notes in Computer Science

Pages from

182

Pages to

193

Pages count

12

URL

http://www.springerlink.com/content/e47453258l284p60/fulltext.pdf

BibTex

@inproceedings{BUT91456,
  author="Michaela {Drahošová} and Lukáš {Sekanina}",
  title="Coevolution in Cartesian Genetic Programming",
  booktitle="Proc. of the 15th European Conference on Genetic Programming",
  year="2012",
  series="Lecture Notes in Computer Science",
  volume="7244",
  pages="182--193",
  publisher="Springer Verlag",
  address="Heidelberg",
  doi="10.1007/978-3-642-29139-5\{_}16",
  isbn="978-3-642-29138-8",
  url="http://www.springerlink.com/content/e47453258l284p60/fulltext.pdf"
}