Automatic inference of hierarchical graph models using genetic programming with an application to cortical networks
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gp-bibliography.bib Revision:1.7975
- @InProceedings{Bailey:2013:GECCO,
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author = "Alexander Bailey and Beatrice Ombuki-Berman and
Mario Ventresca",
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title = "Automatic inference of hierarchical graph models using
genetic programming with an application to cortical
networks",
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booktitle = "GECCO '13: Proceeding of the fifteenth annual
conference on Genetic and evolutionary computation
conference",
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year = "2013",
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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",
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isbn13 = "978-1-4503-1963-8",
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pages = "893--900",
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keywords = "genetic algorithms, genetic programming",
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month = "6-10 " # jul,
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organisation = "SIGEVO",
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address = "Amsterdam, The Netherlands",
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DOI = "doi:10.1145/2463372.2463498",
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publisher = "ACM",
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publisher_address = "New York, NY, USA",
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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.",
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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