Detail publikace

Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs

DRAHOŠOVÁ, M. HULVA, J. SEKANINA, L.

Originální název

Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

We investigate coevolutionary Cartesian genetic programming that coevolves fitness predictors in order to diminish the number of target objective vector (TOV) evaluations, needed to obtain a satisfactory solution, to reduce the computational cost of evolution. This paper introduces the use of coevolution of fitness predictors in CGP with a new type of indirectly encoded predictors. Indirectly encoded predictors are operated using the CGP and provide a variable number of TOVs used for solution evaluation during the coevolution. It is shown in 5 symbolic regression problems that the proposed predictors are able to adapt the size of TOVs array in response to a particular training data set.

Klíčová slova

coevolution, cartesian genetic programming, fitness prediction, symbolic regression

Autoři

DRAHOŠOVÁ, M.; HULVA, J.; SEKANINA, L.

Rok RIV

2015

Vydáno

15. 3. 2015

Nakladatel

Springer International Publishing

Místo

Berlin

ISBN

978-3-319-16500-4

Kniha

Genetic Programming

Edice

Lecture Notes in Computer Science

Strany od

113

Strany do

125

Strany počet

13

URL

http://dx.doi.org/10.1007/978-3-319-16501-1_10

BibTex

@inproceedings{BUT119803,
  author="Michaela {Drahošová} and Jiří {Hulva} and Lukáš {Sekanina}",
  title="Indirectly Encoded Fitness Predictors Coevolved with Cartesian Programs",
  booktitle="Genetic Programming",
  year="2015",
  series="Lecture Notes in Computer Science",
  volume="9025",
  pages="113--125",
  publisher="Springer International Publishing",
  address="Berlin",
  doi="10.1007/978-3-319-16501-1\{_}10",
  isbn="978-3-319-16500-4",
  url="http://dx.doi.org/10.1007/978-3-319-16501-1_10"
}