Symbolic regression using abstract expression grammars

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

  author =       "Michael F. Korns",
  title =        "Symbolic regression using abstract expression
  booktitle =    "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
                 Genetic and Evolutionary Computation",
  year =         "2009",
  editor =       "Lihong Xu and Erik D. Goodman and Guoliang Chen and 
                 Darrell Whitley and Yongsheng Ding",
  bibsource =    "DBLP,",
  pages =        "859--862",
  address =      "Shanghai, China",
  organisation = "SigEvo",
  DOI =          "doi:10.1145/1543834.1543960",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        jun # " 12-14",
  isbn13 =       "978-1-60558-326-6",
  keywords =     "genetic algorithms, genetic programming, Poster, PSO,
  abstract =     "Expression Grammars have the potential to integrate
                 Genetic Algorithms, Genetic Programming, Swarm
                 Intelligence, and Differential Evolution into a
                 seamlessly unified array of tools for use in symbolic
                 regression. The features of abstract expression
                 grammars are explored, examples of implementations are
                 provided, and the beneficial effects of abstract
                 expression grammars are tested with several published
                 nonlinear regression problems.",
  notes =        "p 860 {"}Abstract constants represent placeholders for
                 real numbers which are to be optimized{"}. Abstract
                 features inputs again optimised by PSO, DE, etc.
                 Abstract functions chosen by GA etc from predefined
                 list of functions. User uses grammar to decide how much
                 freedom or how constrained system is to be. 10000
                 training examples.

                 Also known as \cite{DBLP:conf/gecco/Korns09}. Part of

Genetic Programming entries for Michael Korns