Evolving Graphs by Graph Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Atkinson:2018:EuroGP,
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author = "Timothy Atkinson and Detlef Plump and Susan Stepney",
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title = "Evolving Graphs by Graph Programming",
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booktitle = "EuroGP 2018: Proceedings of the 21st European
Conference on Genetic Programming",
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year = "2018",
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month = "4-6 " # apr,
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editor = "Mauro Castelli and Lukas Sekanina and
Mengjie Zhang and Stefano Cagnoni and Pablo Garcia-Sanchez",
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series = "LNCS",
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volume = "10781",
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publisher = "Springer Verlag",
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address = "Parma, Italy",
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pages = "35--51",
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organisation = "EvoStar, Species",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming",
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isbn13 = "978-3-319-77552-4",
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URL = "http://eprints.whiterose.ac.uk/126500/1/AtkinsonPlumpStepney.EuroGP.18.pdf",
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DOI = "doi:10.1007/978-3-319-77553-1_3",
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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.",
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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