Publication Details

Graph-based Genetic Programming

KALKREUTH, R.; DAL PICCOL SOTTO, L.; VAŠÍČEK, Z. Graph-based Genetic Programming. In GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference. Boston: Association for Computing Machinery, 2022. p. 958-982. ISBN: 978-1-4503-9268-6.
Czech title
Varianty genetického programování využívající grafové reprezentace
Type
conference paper
Language
English
Authors
Kalkreuth Roman, M.Sc., Ph.D.
Dal Piccol Sotto Léo Françoso
Vašíček Zdeněk, doc. Ing., Ph.D. (DCSY)
Keywords

Genetic Programming, Cartesian Genetic Programming, Linear Genetic Programming,
Parallel Distributed Genetic Programming

Abstract

Although the classical way to represent programs in Genetic Programming (GP) is
by means of an expression tree, different GP variants with alternative
representations have been proposed throughout the years. One such representation
is the Directed Acyclic Graph (DAG), adopted by methods like Cartesian Genetic
Programming (CGP), Linear Genetic Programming (LGP), Parallel Distributed Genetic
Programming (PDGP), and, more recently, Evolving Graphs by Graph Programming
(EGGP). The aim of this tutorial is to consider this methods from a unified
perspective as graph-based GP, present their historical background,
representation features, operators, applications, and available implementations.

Published
2022
Pages
958–982
Proceedings
GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference
Conference
Genetic and Evolutionary Computations Conference 2022, Boston, US
ISBN
978-1-4503-9268-6
Publisher
Association for Computing Machinery
Place
Boston
DOI
10.1145/3520304.3533657
UT WoS
001035469400230
EID Scopus
2-s2.0-85136810641
BibTeX 
@inproceedings{BUT180545,
  author="Roman {Kalkreuth} and Léo Françoso {Dal Piccol Sotto} and Zdeněk {Vašíček}",
  title="Graph-based Genetic Programming",
  booktitle="GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference",
  year="2022",
  pages="958--982",
  publisher="Association for Computing Machinery",
  address="Boston",
  doi="10.1145/3520304.3533657",
  isbn="978-1-4503-9268-6"
}
Back to top