Automatic generation of graph models for complex networks by genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{Bailey:2012:GECCO,
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author = "Alexander Bailey and Mario Ventresca and
Beatrice Ombuki-Berman",
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title = "Automatic generation of graph models for complex
networks by genetic programming",
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booktitle = "GECCO '12: Proceedings of the fourteenth international
conference on Genetic and evolutionary computation
conference",
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year = "2012",
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editor = "Terry Soule and Anne Auger and Jason Moore and
David Pelta and Christine Solnon and Mike Preuss and
Alan Dorin and Yew-Soon Ong and Christian Blum and
Dario Landa Silva and Frank Neumann and Tina Yu and
Aniko Ekart and Will Browne and Tim Kovacs and
Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and
Giovanni Squillero and Nicolas Bredeche and
Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and
Martin Pelikan and Silja Meyer-Nienberg and
Christian Igel and Greg Hornby and Rene Doursat and
Steve Gustafson and Gustavo Olague and Shin Yoo and
John Clark and Gabriela Ochoa and Gisele Pappa and
Fernando Lobo and Daniel Tauritz and Jurgen Branke and
Kalyanmoy Deb",
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isbn13 = "978-1-4503-1177-9",
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pages = "711--718",
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keywords = "genetic algorithms, genetic programming",
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month = "7-11 " # jul,
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organisation = "SIGEVO",
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address = "Philadelphia, Pennsylvania, USA",
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URL = "http://cs.adelaide.edu.au/~brad/papers/alexanderThielPeacock.pdf",
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DOI = "doi:10.1145/2330163.2330263",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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abstract = "Complex networks have attracted a large amount of
research attention, especially over the past decade,
due to their prevalence and importance in our daily
lives. Numerous human-designed models have been
proposed that aim to capture and model different
network structures, for the purpose of improving our
understanding the real-life phenomena and its dynamics
in different situations. Groundbreaking work in
genetics, medicine, epidemiology, neuroscience,
telecommunications, social science and drug discovery,
to name some examples, have directly resulted. Because
the graph models are human made (a very time consuming
process) using a small subset of example graphs, they
often exhibit inaccuracies when used to model similar
structures. This paper represents the first exploration
into the use of genetic programming for automating the
discovery and algorithm design of graph models,
representing a totally new approach with great
interdisciplinary application potential. We present
exciting initial results that show the potential of GP
to replicate existing complex network algorithms.",
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notes = "alexanderThielPeacock.pdf corrected version (fixed
typo in background resistivity)
Entered for 2013 HUMIES GECCO 2013
Also known as \cite{2330263} GECCO-2012 A joint meeting
of the twenty first international conference on genetic
algorithms (ICGA-2012) and the seventeenth annual
genetic programming conference (GP-2012)",
- }
Genetic Programming entries for
Alexander Bailey
Mario Ventresca
Beatrice Ombuki-Berman
Citations