Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Koncal:2019:EuroGP,
-
author = "Ondrej Koncal and Lukas Sekanina",
-
title = "Cartesian Genetic Programming as an Optimizer of
Programs Evolved with Geometric Semantic Genetic
Programming",
-
booktitle = "EuroGP 2019: Proceedings of the 22nd European
Conference on Genetic Programming",
-
year = "2019",
-
month = "24-26 " # apr,
-
editor = "Lukas Sekanina and Ting Hu and Nuno Lourenco",
-
series = "LNCS",
-
volume = "11451",
-
publisher = "Springer Verlag",
-
address = "Leipzig, Germany",
-
pages = "98--113",
-
organisation = "EvoStar, Species",
-
keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
-
isbn13 = "978-3-030-16669-4",
-
URL = "https://www.springer.com/us/book/9783030166694",
-
DOI = "doi:10.1007/978-3-030-16670-0_7",
-
size = "16 pages",
-
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 92.4percent to 99.9percent.",
-
notes = "http://www.evostar.org/2019/cfp_eurogp.php#abstracts
Part of \cite{Sekanina:2019:GP} EuroGP'2019 held in
conjunction with EvoCOP2019, EvoMusArt2019 and
EvoApplications2019",
- }
Genetic Programming entries for
Ondrej Koncal
Lukas Sekanina
Citations