Parse-matrix evolution for symbolic regression

Created by W.Langdon from gp-bibliography.bib Revision:1.4340

  author =       "Changtong Luo and Shao-Liang Zhang",
  title =        "Parse-matrix evolution for symbolic regression",
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2012",
  volume =       "25",
  number =       "6",
  pages =        "1182--1193",
  ISSN =         "0952-1976",
  DOI =          "doi:10.1016/j.engappai.2012.05.015",
  URL =          "",
  keywords =     "genetic algorithms, genetic programming, Data
                 analysis, Symbolic regression, Grammatical evolution,
                 Artificial intelligence, Evolutionary computation",
  abstract =     "Data-driven model is highly desirable for industrial
                 data analysis in case the experimental model structure
                 is unknown or wrong, or the concerned system has
                 changed. Symbolic regression is a useful method to
                 construct the data-driven model (regression equation).
                 Existing algorithms for symbolic regression such as
                 genetic programming and grammatical evolution are
                 difficult to use due to their special target
                 programming language (i.e., LISP) or additional
                 function parsing process. In this paper, a new
                 evolutionary algorithm, parse-matrix evolution (PME),
                 for symbolic regression is proposed. A chromosome in
                 PME is a parse-matrix with integer entries. The mapping
                 process from the chromosome to the regression equation
                 is based on a mapping table. PME can easily be
                 implemented in any programming language and free to
                 control. Furthermore, it does not need any additional
                 function parsing process. Numerical results show that
                 PME can solve the symbolic regression problems

Genetic Programming entries for Changtong Luo Shao-Liang Zhang