Automatic generation of graph models for complex networks by genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4496

  author =       "Alexander Bailey and Mario Ventresca and 
                 Beatrice Ombuki-Berman",
  title =        "Automatic generation of graph models for complex
                 networks by genetic programming",
  booktitle =    "GECCO '12: Proceedings of the fourteenth international
                 conference on Genetic and evolutionary computation
  year =         "2012",
  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",
  isbn13 =       "978-1-4503-1177-9",
  pages =        "711--718",
  keywords =     "genetic algorithms, genetic programming",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  URL =          "",
  DOI =          "doi:10.1145/2330163.2330263",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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.",
  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