Detail publikace

Coevolution in Cartesian Genetic Programming

DRAHOŠOVÁ, M. SEKANINA, L.

Originální název

Coevolution in Cartesian Genetic Programming

Typ

článek ve sborníku mimo WoS a Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

DRAHOŠOVÁ, M.; SEKANINA, L.

Rok RIV

2012

Vydáno

23. 3. 2012

Nakladatel

Springer Verlag

Místo

Heidelberg

ISBN

978-3-642-29138-8

Kniha

Proc. of the 15th European Conference on Genetic Programming

Edice

Lecture Notes in Computer Science

Strany od

182

Strany do

193

Strany počet

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"
}