Graphical models and what they reveal about GP when it solves a symbolic regression problem

Created by W.Langdon from gp-bibliography.bib Revision:1.4009

  author =       "Erik Hemberg and Kalyan Veeramachaneni and 
                 Una-May O'Reilly",
  title =        "Graphical models and what they reveal about GP when it
                 solves a symbolic regression problem",
  booktitle =    "GECCO 2012 Symbolic regression and modeling workshop",
  year =         "2012",
  editor =       "Steven Gustafson and Ekaterina Vladislavleva",
  isbn13 =       "978-1-4503-1178-6",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "493--494",
  month =        "7-11 " # jul,
  organisation = "SIGEVO",
  address =      "Philadelphia, Pennsylvania, USA",
  DOI =          "doi:10.1145/2330784.2330860",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "We introduce the notion of using graphical models as a
                 new and complementary means of understanding genetic
                 programming dynamics (along with statistics such as
                 mean tree size, etc). Graphical models reveal the
                 dependency structure of the multivariate distribution
                 associated with functions and terminals in solution
                 structures. This information is more semantically
                 rather than syntax oriented. As a first step, using the
                 Pagie-2D problem as our exemplar, we present the
                 generation and inter-generation dynamics of genetic
                 programming in terms of graphical models that are
                 largely unrestricted in structure. Open for discussion
                 are questions such as: should a estimation of
                 distribution genetic programming algorithm mimic
                 standard genetic programming's search bias in terms of
                 tree size and shape? And, does graphical model analysis
                 indicate a better way to control the search bias for
                 symbolic regression - by operator design, size control,
                 bloat control or other means?",
  notes =        "Also known as \cite{2330860} Distributed at

                 ACM Order Number 910122.",

Genetic Programming entries for Erik Hemberg Kalyan Veeramachaneni Una-May O'Reilly