Improving Symbolic Regression through a semantics-driven framework

Created by W.Langdon from gp-bibliography.bib Revision:1.4216

  author =       "Quang Nhat Huynh and Hemant Kumar Singh and 
                 Tapabrata Ray",
  booktitle =    "2016 IEEE Symposium Series on Computational
                 Intelligence (SSCI)",
  title =        "Improving Symbolic Regression through a
                 semantics-driven framework",
  year =         "2016",
  abstract =     "The process of identifying analytical relationships
                 among variables and responses in observed data is
                 commonly referred to as Symbolic Regression (SR).
                 Genetic Programming is one of the commonly used
                 approaches for SR, which operates by evolving
                 expressions. Such relationships could be explicit or
                 implicit in nature, of which the former has been more
                 extensively studied in literature. Even though
                 extensive studies have been done in SR, the fundamental
                 challenges such as bloat, loss of diversity and
                 accurate determination of coefficients still persist.
                 Recently, semantics and multi-objective formulation
                 have been suggested as potential tools to alleviate
                 these issues by building more intelligence in the
                 search process. However, studies along both these
                 directions have been in isolation and applied only to
                 selected components of SR so far. In this paper, we
                 intend to build a framework that integrates semantics
                 deeper into more components of SR. The framework could
                 be operated in conventional single objective as well as
                 multi-objective mode and is capable of dealing with
                 both explicit and implicit functions. Semantics are
                 used in the proposed framework for improving
                 compactness and diversity of expressions, crossover and
                 local exploitation. Numerical experiments are presented
                 on a set of benchmark problems to demonstrate the
                 strengths of the proposed approach.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2016.7849941",
  month =        dec,
  notes =        "Also known as \cite{7849941}",

Genetic Programming entries for Quang Nhat Huynh Hemant Kumar Singh Tapabrata Ray