EGIPSYS: an Enhanced Gene Expression Programming Approach for Symbolic Regression Problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3963

@Article{Lopes:2004:AMCS,
  author =       "Heitor S. Lopes and Wagner R. Weinert",
  title =        "EGIPSYS: an Enhanced Gene Expression Programming
                 Approach for Symbolic Regression Problems",
  journal =      "International Journal of Applied Mathematics and
                 Computer Science",
  year =         "2004",
  volume =       "14",
  number =       "3",
  pages =        "375--384",
  month =        sep,
  note =         "Special Issue: Evolutionary Computation",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming, evolutionary computation,
                 symbolic regression, mathematical modeling, systems
                 identification",
  ISSN =         "1641-876X",
  URL =          "https://www.amcs.uz.zgora.pl/?action=paper&paper=208",
  URL =          "https://www.amcs.uz.zgora.pl/?action=download&pdf=AMCS_2004_14_3_7.pdf",
  size =         "10 pages",
  abstract =     "This enhanced system, called EGIPSYS, has features
                 specially suited to deal with symbolic regression
                 problems. Amongst the new features implemented in
                 EGIPSYS are: new selection methods, chromosomes of
                 variable length, a new approach to manipulating
                 constants, new genetic operators and an adaptable
                 fitness function. All the proposed improvements were
                 tested separately, and proved to be advantageous over
                 the basic GEP. EGIPSYS was also applied to four
                 difficult identification problems and its performance
                 was compared with a traditional implementation of
                 genetic programming (LilGP). Overall, EGIPSYS was able
                 to obtain consistently better results than the system
                 using genetic programming, finding less complex
                 solutions with less computational effort. The success
                 obtained suggests the adaptation and extension of the
                 system to other classes of problems.",
  notes =        "AMCS University of Zielona Gora Press

                 Centro Federal de Educacao Tecnologica do Parana /
                 CPGEI Av. 7 de setembro, 3165, 80230-901 Curitiba (PR),
                 Brazil

                 March 2014 amc1434.pdf appears to be a different
                 paper",
}

Genetic Programming entries for Heitor Silverio Lopes Wagner R Weinert

Citations