Evolving Graphs by Graph Programming

Timothy Atkinson, Detlef Plump, Susan Stepney

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Rule-based graph programming is a deep and rich topic. We present an approach to exploiting the power of graph programming as a representation and as an execution medium in an evolutionary algorithm (EGGP). We demonstrate this power in comparison with Cartesian Genetic Programming (CGP), showing that it is significantly more efficient in terms of fitness evaluations on some classic benchmark problems. We hypothesise that this is due to its ability to exploit the full graph structure, leading to a richer mutation set, and outline future work to test this hypothesis, and to exploit further the power of graph programming within an EA.

Original languageEnglish
Title of host publicationGenetic Programming - 21st European Conference, EuroGP 2018, Proceedings
EditorsStefano Cagnoni, Mengjie Zhang, Pablo Garcia-Sanchez, Mauro Castelli, Lukas Sekanina
Number of pages17
ISBN (Print)9783319775524
Publication statusPublished - 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10781 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Bibliographical note

© Springer-Verlag Berlin Heidelberg 2018. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details.

Cite this