A Semantics based Symbolic Regression Framework for Mining Explicit and Implicit Equations from Data

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

  author =       "Quang Nhat Huynh and Hemant Kumar Singh and 
                 Tapabrata Ray",
  title =        "A Semantics based Symbolic Regression Framework for
                 Mining Explicit and Implicit Equations from Data",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  pages =        "103--104",
  month =        "20-24 " # jul,
  keywords =     "genetic algorithms, genetic programming: Poster",
  organisation = "SIGEVO",
  address =      "Denver, USA",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  isbn13 =       "978-1-4503-4323-7",
  DOI =          "doi:10.1145/2908961.2908989",
  abstract =     "Symbolic Regression (SR) is commonly used to identify
                 relationships among variables and responses in a data
                 in the form of analytical, preferably compact
                 expressions. Genetic Programming (GP) is one of the
                 common ways to perform SR. Such relationships could be
                 represented using explicit or implicit expressions, of
                 which the former has been more extensively studied in
                 literature. Some of the key challenges that face SR are
                 bloat, loss of diversity, and accurate determination of
                 coefficients. More recently, semantics and
                 multi-objective formulations have been suggested as
                 potential tools to build 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 traditional single objective as well as
                 multi-objective mode and is capable of dealing with
                 both explicit and implicit functions. The constituent
                 modules use semantics for compaction of expressions,
                 maintaining diversity by identifying unique
                 individuals, crossover and local exploitation. A
                 comparison of obtained results with those from existing
                 semantics-based and multi-objective approach
                 demonstrates the advantages of the proposed
  notes =        "Distributed at GECCO-2016.",

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