Publication detail

Plastic Fitness Predictors Coevolved with Cartesian Programs

WIGLASZ, M. DRAHOŠOVÁ, M.

Original Title

Plastic Fitness Predictors Coevolved with Cartesian Programs

Type

conference paper

Language

English

Original Abstract

Coevolution of fitness predictors, which are a small sample of all training data for a particular task, was successfully used to reduce the computational cost of the design performed by cartesian genetic programming. However, it is necessary to specify the most advantageous number of fitness cases in predictors, which differs from task to task. This paper proposes to introduce a new type of directly encoded fitness predictors inspired by the principles of phenotypic plasticity. The size of the coevolved fitness predictor is adapted in response to the phase of learning that the program evolution goes through. It is shown in 5 symbolic regression tasks that the proposed algorithm is able to adapt the number of fitness cases in predictors in response to the solved task and the program evolution flow.

Keywords

fitness predictors, cartesian genetic programming, coevolution, phenotypic plasticity

Authors

WIGLASZ, M.; DRAHOŠOVÁ, M.

Released

30. 3. 2016

Publisher

Springer International Publishing

Location

Berlin

ISBN

978-3-319-30667-4

Book

19th European Conference on Genetic programming

Edition

Lecture Notes in Computer Science

Pages from

164

Pages to

179

Pages count

16

URL

https://www.fit.vut.cz/research/publication/11001/

BibTex

@inproceedings{BUT130922,
  author="Michal {Wiglasz} and Michaela {Drahošová}",
  title="Plastic Fitness Predictors Coevolved with Cartesian Programs",
  booktitle="19th European Conference on Genetic programming",
  year="2016",
  series="Lecture Notes in Computer Science",
  volume="9594",
  pages="164--179",
  publisher="Springer International Publishing",
  address="Berlin",
  doi="10.1007/978-3-319-30668-1\{_}11",
  isbn="978-3-319-30667-4",
  url="https://www.fit.vut.cz/research/publication/11001/"
}