Prof. Ing. Lukáš Sekanina, Ph.D.

KONČAL Ondřej and SEKANINA Lukáš. 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, pp. 98-113. ISBN 978-3-030-16669-4.
Publication language:english
Original title:Cartesian Genetic Programming as an Optimizer of Programs Evolved with Geometric Semantic Genetic Programming
Title (cs):Kartézské genetické programování jako optimalizátor programů evolvovaných pomocí geometrického sémantického genetického programování
Pages:98-113
Proceedings:Genetic Programming 22nd European Conference, EuroGP 2019
Conference:22nd European Conference on Genetic Programming
Place:Cham, CH
Year:2019
ISBN:978-3-030-16669-4
DOI:10.1007/978-3-030-16670-0_7
Publisher:Springer International Publishing
Keywords
Cartesian Genetic Programming, Geometric Semantic Genetic Programming, symbolic regression, semantics 
Annotation
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%.
BibTeX:
@INPROCEEDINGS{
   author = {Ond{\v{r}}ej Kon{\v{c}}al and Luk{\'{a}}{\v{s}}
	Sekanina},
   title = {Cartesian Genetic Programming as an Optimizer of
	Programs Evolved with Geometric Semantic Genetic
	Programming},
   pages = {98--113},
   booktitle = {Genetic Programming 22nd European Conference, EuroGP 2019},
   year = {2019},
   location = {Cham, CH},
   publisher = {Springer International Publishing},
   ISBN = {978-3-030-16669-4},
   doi = {10.1007/978-3-030-16670-0_7},
   language = {english},
   url = {http://www.fit.vutbr.cz/research/view_pub.php.en?id=11859}
}

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