Introducing Graphical Models to Analyze Genetic Programming Dynamics

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@InProceedings{Hemberg:2013:foga,
  author =       "Erik Hemberg and Kalyan Veeramachaneni and 
                 Constantin Berzan and Una-May O'Reilly",
  title =        "Introducing Graphical Models to Analyze Genetic
                 Programming Dynamics",
  booktitle =    "Foundations of Genetic Algorithms",
  year =         "2013",
  editor =       "Frank Neumann and Kenneth {De Jong}",
  pages =        "75--86",
  address =      "Adelaide, Australia",
  month =        "16-20 " # jan,
  organisation = "SigEvo",
  publisher =    "ACM",
  keywords =     "genetic algorithms, genetic programming, Bayesian
                 networks, graphical models",
  isbn13 =       "978-1-4503-1990-4",
  URL =          "http://doi.acm.org/10.1145/2460239.2460247",
  DOI =          "doi:10.1145/2460239.2460247",
  acmid =        "2460247",
  size =         "12 pages",
  abstract =     "We propose graphical models as a new means of
                 understanding genetic programming dynamics. Herein, we
                 describe how to build an unbiased graphical model from
                 a population of genetic programming trees. Graphical
                 models both express information about the conditional
                 dependency relations among a set of random variables
                 and they support probabilistic inference regarding the
                 likelihood of a random variable's outcome. We focus on
                 the former information: by their structure, graphical
                 models reveal structural dependencies between the nodes
                 of genetic programming trees. We identify graphical
                 model properties of potential interest in this regard -
                 edge quantity and dependency among nodes expressed in
                 terms of family relations. Using a simple symbolic
                 regression problem we generate a graphical model of the
                 population each generation. Then we interpret the
                 graphical models with respect to conventional knowledge
                 about the influence of subtree crossover and mutation
                 upon tree structure.",
  notes =        "Also known as \cite{Hemberg:2013:IGM:2460239.2460247}
                 http://www.sigevo.org/foga-2013/index.html",
}

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

Citations