Grammar-Guided Evolutionary Construction of Bayesian Networks

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

@InProceedings{Font:2011:IWINAC,
  author =       "Jose Font and Daniel Manrique and Eduardo Pascua",
  title =        "Grammar-Guided Evolutionary Construction of Bayesian
                 Networks",
  booktitle =    "Proceedings of the 4th International Work-Conference
                 on the Interplay Between Natural and Artificial
                 Computation, IWINAC 2011, Part I",
  year =         "2011",
  editor =       "Jose Manuel Ferrandez and 
                 Jose Ramon {Alvarez Sanchez} and Felix {de la Paz} and F. Javier Toledo",
  series =       "Lecture Notes in Computer Science",
  pages =        "60--69",
  volume =       "6686",
  address =      "La Palma, Canary Islands, Spain",
  month =        may # " 30-" # jun # " 3",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-21343-4",
  DOI =          "doi:10.1007/978-3-642-21344-1_7",
  abstract =     "This paper proposes the EvoBANE system. EvoBANE
                 automatically generates Bayesian networks for solving
                 special-purpose problems. EvoBANE evolves a population
                 of individuals that codify Bayesian networks until it
                 finds near optimal individual that solves a given
                 classification problem. EvoBANE has the flexibility to
                 modify the constraints that condition the solution
                 search space, self-adapting to the specifications of
                 the problem to be solved. The system extends the GGEAS
                 architecture. GGEAS is a general-purpose grammar-guided
                 evolutionary automatic system, whose modular structure
                 favours its application to the automatic construction
                 of intelligent systems. EvoBANE has been applied to two
                 classification benchmark datasets belonging to
                 different application domains, and statistically
                 compared with a genetic algorithm performing the same
                 tasks. Results show that the proposed system performed
                 better, as it manages different complexity constraints
                 in order to find the simplest solution that best solves
                 every problem.",
  affiliation =  "Departamento de Inteligencia Artificial, Universidad
                 Politecnica de Madrid. Campus de Montegancedo, 28660
                 Boadilla del Monte, Spain",
}

Genetic Programming entries for Jose M Font Daniel Manrique Gamo Eduardo Pascua

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