Sequential Symbolic Regression with Genetic Programming

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

@InProceedings{Oliveira:2014:GPTP,
  author =       "Luiz Otavio V. B. Oliveira and 
                 Fernando E. B. Otero and Gisele L. Pappa and Julio Albinati",
  title =        "Sequential Symbolic Regression with Genetic
                 Programming",
  booktitle =    "Genetic Programming Theory and Practice XII",
  year =         "2014",
  editor =       "Rick Riolo and William P. Worzel and Mark Kotanchek",
  series =       "Genetic and Evolutionary Computation",
  pages =        "73--90",
  address =      "Ann Arbor, USA",
  month =        "8-10 " # may,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Symbolic
                 Regression, Semantic Genetic Programming, Geometric
                 Semantic Crossover, Problem Transformation",
  isbn13 =       "978-3-319-16029-0",
  DOI =          "doi:10.1007/978-3-319-16030-6_5",
  abstract =     "This chapter describes the Sequential Symbolic
                 Regression (SSR) method, a new strategy for function
                 approximation in symbolic regression. The SSR method is
                 inspired by the sequential covering strategy from
                 machine learning, but instead of sequentially reducing
                 the size of the problem being solved, it sequentially
                 transforms the original problem into potentially
                 simpler problems. This transformation is performed
                 according to the semantic distances between the desired
                 and obtained outputs and a geometric semantic operator.
                 The rationale behind SSR is that, after generating a
                 suboptimal function f via symbolic regression, the
                 output errors can be approximated by another function,
                 in a subsequent iteration. The method was tested in
                 eight polynomial functions, and compared with canonical
                 genetic programming (GP) and geometric semantic genetic
                 programming (SGP). Results showed that SSR
                 significantly outperforms SGP and presents no
                 statistical difference from GP. More importantly, they
                 show the potential of the proposed approach: an
                 effective way of applying geometric semantic operators
                 to combine different (partial) solutions, and at the
                 same time, avoiding the exponential growth problem
                 arising from the use of semantic operators.",
  notes =        "http://cscs.umich.edu/gptp-workshops/

                 Part of \cite{Riolo:2014:GPTP} published after the
                 workshop in 2015",
}

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

Citations