An Improved Single Node Genetic Programming for Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Kubalik:2015:ECTA,
  author =       "Jiri Kubalik and Robert Babuska",
  title =        "An Improved Single Node Genetic Programming for
                 Symbolic Regression",
  booktitle =    "Proceedings of the 7th International Joint Conference
                 on Computational Intelligence, ECTA 2015",
  year =         "2015",
  editor =       "Agostinho Rosa and Juan Julian Merelo and 
                 Antonio Dourado and Jose M. Cadenas and Kurosh Madani and 
                 Antonio Ruano and Joaquim Filipe",
  pages =        "244--251",
  address =      "Lisbon, Portugal",
  month =        "12-14 " # nov,
  organisation = "INSTICC - Institute for Systems and Technologies of
                 Information, Control and Communication, IFAC -
                 International Federation of Automatic Control, IEEE SMC
                 - IEEE Systems, Man and Cybernetics Society",
  publisher =    "SCITEPRESS - Science and Technology Publications",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 Regression. Single Node Genetic Programming",
  size =         "8 pages",
  abstract =     "This paper presents a first step of our research on
                 designing an effective and efficient GP-based method
                 for solving the symbolic regression. We have proposed
                 three extensions of the standard Single Node GP, namely
                 (1) a selection strategy for choosing nodes to be
                 mutated based on the depth of the nodes, (2) operators
                 for placing a compact version of the best tree to the
                 beginning and to the end of the population, and (3) a
                 local search strategy with multiple mutations applied
                 in each iteration. All the proposed modifications have
                 been experimentally evaluated on three symbolic
                 regression problems and compared with standard GP and
                 SNGP. The achieved results are promising showing the
                 potential of the proposed modifications to
                 significantly improve the performance of the SNGP
                 algorithm.",
  notes =        "

                 Contact: Ana Margarida
                 Guerreiro

                 aguerreiro@insticc.org

                 also known as \cite{7529330}",
}

Genetic Programming entries for Jiri Kubalik Robert Babuska

Citations