Solving the Unknown Complexity Formula Problem with Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4020

  author =       "Rayco Batista and Eduardo Segredo and 
                 Carlos Segura and Coromoto Leon and Casiano Rodriguez",
  title =        "Solving the Unknown Complexity Formula Problem with
                 Genetic Programming",
  bibdate =      "2013-06-25",
  bibsource =    "DBLP,
  booktitle =    "Advances in Computational Intelligence - 12th
                 International Work-Conference on Artificial Neural
                 Networks, {IWANN} 2013, Puerto de la Cruz, Tenerife,
                 Spain, June 12-14, 2013, Proceedings, Part {I}",
  publisher =    "Springer",
  year =         "2013",
  volume =       "7902",
  editor =       "Ignacio Rojas and Gonzalo Joya Caparr{\'o}s and 
                 Joan Cabestany",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-38678-7",
  pages =        "232--240",
  series =       "Lecture Notes in Computer Science",
  URL =          "",
  DOI =          "doi:10.1007/978-3-642-38679-4_22",
  abstract =     "The Unknown Complexity Formula Problem (ucfp) is a
                 particular case of the symbolic regression problem in
                 which an analytical complexity formula that fits with
                 data obtained by multiple executions of certain
                 algorithm must be given. In this work, a set of
                 modifications has been added to the standard Genetic
                 Programming (GP) algorithm to deal with the ucfp. This
                 algorithm has been applied to a set of well-known
                 benchmark functions of the symbolic regression problem.
                 Moreover, a real case of the ucfp has been tackled.
                 Experimental evaluation has demonstrated the good
                 behaviour of the proposed approach in obtaining high
                 quality solutions, even for a real instance of the
                 ucfp. Finally, it is worth pointing out that the best
                 published results for the majority of benchmark
                 functions have been improved.",

Genetic Programming entries for Rayco Batista Eduardo Segredo Carlos Segura Coromoto Leon Casiano Rodriguez