Demonstrating the Power of Object-Oriented Genetic Programming via the Inference of Graph Models for Complex Networks

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Medland:2014:NaBIC,
  author =       "Michael Medland and Kyle Harrison and 
                 Beatrice Ombuki-Berman",
  title =        "Demonstrating the Power of Object-Oriented Genetic
                 Programming via the Inference of Graph Models for
                 Complex Networks",
  booktitle =    "Sixth World Congress on Nature and Biologically
                 Inspired Computing",
  year =         "2014",
  editor =       "Ana Maria Madureira and Ajith Abraham and 
                 Emilio Corchado and Leonilde Varela and Azah Kamilah Muda and 
                 Choo yun Huoy",
  pages =        "305--311",
  address =      "Porto, Portugal",
  month =        "30 " # jul # " - 1 " # jul,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4799-5937-2/14",
  DOI =          "doi:10.1109/NaBIC.2014.6921896",
  abstract =     "Traditionally, GP used a single tree-based
                 representation which does not lend itself well to
                 state-based programs or multiple behaviours. To
                 alleviate this drawback, object-oriented GP (OOGP)
                 introduced a means of evolving programs with multiple
                 behaviours which could be easily extended to
                 state-based programs. However, the production of
                 programs which allowed embedded knowledge and produced
                 readable code was still not easily addressed using the
                 OOGP methodology. Exemplified through the evolution of
                 graph models for complex networks, this paper
                 demonstrates the benefits of a new approach to OOGP
                 inspired by abstract classes and linear GP.
                 Furthermore, the new approach to OOGP, named
                 LinkableGP, facilitates the embedding of expert
                 knowledge while also maintaining the benefits of
                 OOGP.",
  notes =        "NaBIC 2014 http://www.mirlabs.net/nabic14/",
}

Genetic Programming entries for Michael Medland Kyle Robert Harrison Beatrice Ombuki-Berman

Citations