GEARNet: grammatical evolution with artificial regulatory networks

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Rui L. Lopes and Ernesto Costa",
  title =        "{GEARNet}: grammatical evolution with artificial
                 regulatory networks",
  booktitle =    "GECCO '13: Proceeding of the fifteenth annual
                 conference on Genetic and evolutionary computation
  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 =        "973--980",
  keywords =     "genetic algorithms, genetic programming, grammatical
  month =        "6-10 " # jul,
  organisation = "SIGEVO",
  address =      "Amsterdam, The Netherlands",
  DOI =          "doi:10.1145/2463372.2463490",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "The Central Dogma of Biology states that genes made
                 proteins that made us. This principle has been revised
                 in order to incorporate the role played by a multitude
                 of regulatory mechanisms that are fundamental in both
                 the processes of inheritance and development.
                 Evolutionary Computation algorithms are inspired by the
                 theories of evolution and development, but most of the
                 computational models proposed so far rely on a simple
                 genotype to phenotype mapping. During the last years
                 some researchers advocate the need to explore
                 computationally the new biological understanding and
                 have proposed different gene expression models to be
                 incorporated in the algorithms.Two examples are the
                 Artificial Regulatory Network (ARN) model, first
                 proposed by Wolfgang Banzhaf, and the Grammatical
                 Evolution (GE) model, introduced by Michael O'Neill and
                 Conor Ryan. In this paper, we show how a modified
                 version of the ARN can be combined with the GE
                 approach, in the context of automatic program
                 generation. More precisely, we rely on the ARN to
                 control the gene expression process ending in an
                 ordered set of proteins, and on the GE to build, guided
                 by a grammar, a computational structure from that set.
                 As a proof of concept we apply the hybrid model to two
                 benchmark problems and show that it is effective in
                 solving them.",
  notes =        "Also known as \cite{2463490} 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 Rui Lopes Ernesto Costa