Revisiting the Sequential Symbolic Regression Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Luiz Otavio V. B. Oliveira and 
                 Fernando E. B. Otero and Luis F. Miranda and Gisele L. Pappa",
  booktitle =    "2016 5th Brazilian Conference on Intelligent Systems
  title =        "Revisiting the Sequential Symbolic Regression Genetic
  year =         "2016",
  pages =        "163--168",
  abstract =     "Sequential Symbolic Regression (SSR) is a technique
                 that recursively induces functions over the error of
                 the current solution, concatenating them in an attempt
                 to reduce the error of the resulting model. As proof of
                 concept, the method was previously evaluated in
                 one-dimensional problems and compared with canonical
                 Genetic Programming (GP) and Geometric Semantic Genetic
                 Programming (GSGP). In this paper we revisit SSR
                 exploring the method behaviour in higher dimensional,
                 larger and more heterogeneous datasets. We discuss the
                 difficulties arising from the application of the method
                 to more complex problems, e.g., over fitting, along
                 with suggestions to overcome them. An experimental
                 analysis was conducted comparing SSR to GP and GSGP,
                 showing SSR solutions are smaller than those generated
                 by the GSGP with similar performance and more accurate
                 than those generated by the canonical GP.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/BRACIS.2016.039",
  month =        oct,
  notes =        "Also known as \cite{7839580}",

Genetic Programming entries for Luiz Otavio Vilas Boas Oliveira Fernando Esteban Barril Otero Luis Fernando Miranda Gisele L Pappa