Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Bailey:2013:GECCO,
  author =       "Alexander Bailey and Beatrice Ombuki-Berman and 
                 Mario Ventresca",
  title =        "Automatic inference of hierarchical graph models using
                 genetic programming with an application to cortical
                 networks",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
                 conference",
  year =         "2013",
  editor =       "Christian Blum and Enrique Alba and Anne Auger and 
                 Jaume Bacardit and Josh Bongard and Juergen Branke and 
                 Nicolas Bredeche and Dimo Brockhoff and 
                 Francisco Chicano and Alan Dorin and Rene Doursat and 
                 Aniko Ekart and Tobias Friedrich and Mario Giacobini and 
                 Mark Harman and Hitoshi Iba and Christian Igel and 
                 Thomas Jansen and Tim Kovacs and Taras Kowaliw and 
                 Manuel Lopez-Ibanez and Jose A. Lozano and Gabriel Luque and 
                 John McCall and Alberto Moraglio and 
                 Alison Motsinger-Reif and Frank Neumann and Gabriela Ochoa and 
                 Gustavo Olague and Yew-Soon Ong and 
                 Michael E. Palmer and Gisele Lobo Pappa and 
                 Konstantinos E. Parsopoulos and Thomas Schmickl and Stephen L. Smith and 
                 Christine Solnon and Thomas Stuetzle and El-Ghazali Talbi and 
                 Daniel Tauritz and Leonardo Vanneschi",
  isbn13 =       "978-1-4503-1963-8",
  pages =        "893--900",
  keywords =     "genetic algorithms, genetic programming",
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463498",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The pathways that relay sensory information within the
                 brain form a network of connections, the precise
                 organisation of which is unknown. Communities of
                 neurons can be discerned within this tangled structure,
                 with inhomogeneously distributed connections existing
                 between cortical areas. Classification and modelling of
                 these networks has led to advancements in the
                 identification of unhealthy or injured brains, however,
                 the current models used are known to have major
                 deficiencies. Specifically, the community structure of
                 the cortex is not accounted for in existing algorithms,
                 and it is unclear how to properly design a more
                 representative graph model. It has recently been
                 demonstrated that genetic programming may be useful for
                 inferring accurate graph models, although no study to
                 date has investigated the ability to replicate
                 community structure. In this paper we propose the first
                 GP system for the automatic inference of algorithms
                 capable of generating, to a high accuracy, networks
                 with community structure. We use a common cat cortex
                 data set to highlight the efficacy of our approach. Our
                 experiments clearly show that the inferred graph model
                 generates a more representative network than those
                 currently used in scientific literature.",
  notes =        "Also known as \cite{2463498} GECCO-2013 A joint
                 meeting of the twenty second international conference
                 on genetic algorithms (ICGA-2013) and the eighteenth
                 annual genetic programming conference (GP-2013)",
}

Genetic Programming entries for Alexander Bailey Beatrice Ombuki-Berman Mario Ventresca

Citations