Using traceless genetic programming for solving multi-objective optimization problems

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

  author =       "Mihai Oltean and Crina Grosan",
  title =        "Using traceless genetic programming for solving
                 multi-objective optimization problems",
  journal =      "Journal of Experimental \& Theoretical Artificial
  year =         "2007",
  volume =       "19",
  number =       "3",
  pages =        "227--248",
  email =        "",
  keywords =     "genetic algorithms, genetic programming,
                 multiobjective optimisation",
  ISSN =         "0952-813X",
  URL =          "",
  DOI =          "doi:10.1080/09528130601138273",
  size =         "21 pages",
  abstract =     "Traceless Genetic Programming (TGP) is a Genetic
                 Programming (GP) variant that is used in the cases
                 where the focus is rather the output of the program
                 than the program itself. The main difference between
                 TGP and other GP techniques is that TGP does not
                 explicitly store the evolved computer programs. Two
                 genetic operators are used in conjunction with TGP:
                 crossover and insertion. In this paper we shall focus
                 on how to apply TGP for solving multiobjective
                 optimisation problems which are quite unusual for GP.
                 Each TGP individual stores the output of a computer
                 program (tree) representing a point in the search
                 space. Numerical experiments show that TGP is able to
                 solve very fast and very well the considered test

Genetic Programming entries for Mihai Oltean Crina Grosan