Publication Details

Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming

KONČAL, O.; SEKANINA, L. Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming. In Genetic Programming 22nd European Conference, EuroGP 2019. Cham: Springer International Publishing, 2019. p. 98-113. ISBN: 978-3-030-16669-4.
Czech title
Kartézské genetické programování jako optimalizátor programů evolvovaných pomocí geometrického sémantického genetického programování
Type
conference paper
Language
English
Authors
Končal Ondřej, Ing.
Sekanina Lukáš, prof. Ing., Ph.D. (DCSY)
Keywords

Cartesian Genetic Programming, Geometric Semantic Genetic Programming, symbolic
regression, semantics 

Abstract

In Geometric Semantic Genetic Programming (GSGP), genetic operators directly work
at the level of semantics rather than syntax. It provides many advantages,
including much higher quality of resulting individuals (in terms of error) in
comparison with a common genetic programming. However, GSGP produces extremely
huge solutions that could be difficult to apply in systems with limited resources
such as embedded systems. We propose Subtree Cartesian Genetic Programming (SCGP)
-- a method capable of reducing the number of nodes in the trees generated by
GSGP. SCGP executes a common Cartesian Genetic Programming (CGP) on all
elementary subtrees created by GSGP and on various compositions of these
optimized subtrees in order to create one compact representation of the original
program. SCGP does not guarantee the (exact) semantic equivalence between the CGP
individuals and the GSGP subtrees, but the user can define conditions when
a particular CGP individual is acceptable. We evaluated SCGP on four common
symbolic regression benchmark problems and the obtained node reduction is from
92.4% to 99.9%.

Published
2019
Pages
98–113
Proceedings
Genetic Programming 22nd European Conference, EuroGP 2019
Conference
22nd European Conference on Genetic Programming, Leipzig, DE
ISBN
978-3-030-16669-4
Publisher
Springer International Publishing
Place
Cham
DOI
10.1007/978-3-030-16670-0_7
EID Scopus
2-s2.0-85064926142
BibTeX 
@inproceedings{BUT156847,
  author="Ondřej {Končal} and Lukáš {Sekanina}",
  title="Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming",
  booktitle="Genetic Programming 22nd European Conference, EuroGP 2019",
  year="2019",
  pages="98--113",
  publisher="Springer International Publishing",
  address="Cham",
  doi="10.1007/978-3-030-16670-0\{_}7",
  isbn="978-3-030-16669-4",
  url="https://www.fit.vut.cz/research/publication/11859/"
}
Files
Back to top