Evolving Graphs by Graph Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7970

@InProceedings{Atkinson:2018:EuroGP,

• author = "Timothy Atkinson and Detlef Plump and Susan Stepney",
• title = "Evolving Graphs by Graph Programming",
• booktitle = "EuroGP 2018: Proceedings of the 21st European Conference on Genetic Programming",
• year = "2018",
• month = "4-6 " # apr,
• editor = "Mauro Castelli and Lukas Sekanina and Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
• series = "LNCS",
• volume = "10781",
• publisher = "Springer Verlag",
• address = "Parma, Italy",
• pages = "35--51",
• organisation = "EvoStar, Species",
• keywords = "genetic algorithms, genetic programming, Cartesian Genetic Programming",
• isbn13 = "978-3-319-77552-4",
• URL = "http://eprints.whiterose.ac.uk/126500/1/AtkinsonPlumpStepney.EuroGP.18.pdf",
• DOI = "doi:10.1007/978-3-319-77553-1_3",
• abstract = "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.",
• notes = "Part of \cite{Castelli:2018:GP} EuroGP'2018 held in conjunction with EvoCOP2018, EvoMusArt2018 and EvoApplications2018",

}

Genetic Programming entries for Timothy Atkinson Detlef Plump Susan Stepney

Citations