Publication detail

Graph-based Genetic Programming

KALKREUTH, R. DAL PICCOL SOTTO, L. VAŠÍČEK, Z.

Original Title

Graph-based Genetic Programming

Type

conference paper

Language

English

Original 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.

Keywords

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

Authors

KALKREUTH, R.; DAL PICCOL SOTTO, L.; VAŠÍČEK, Z.

Released

8. 7. 2022

Publisher

Association for Computing Machinery

Location

Boston

ISBN

978-1-4503-9268-6

Book

GECCO 2022 Companion - Proceedings of the 2022 Genetic and Evolutionary Computation Conference

Pages from

958

Pages to

982

Pages count

25

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"
}