Single- and Multi-Objective Genetic Programming: New Runtime Results for SORTING

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  title =        "Single- and Multi-Objective Genetic Programming: New
                 Runtime Results for {SORTING}",
  author =       "Markus Wagner and Frank Neumann",
  pages =        "125--132",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, genetic programming, Theoretical
                 Foundations of Bio-inspired Computation",
  URL =          "",
  DOI =          "doi:10.1109/CEC.2014.6900310",
  size =         "8 pages",
  abstract =     "In genetic programming, the size of a solution is
                 typically not specified in advance and solutions of
                 larger size may have a larger benefit. The flexibility
                 often comes at the cost of the so-called bloat problem:
                 individuals grow without providing additional benefit
                 to the quality of solutions, and the additional
                 elements can block the optimisation process.
                 Consequently, problems that are relatively easy to
                 optimise cannot be handled by variable-length
                 evolutionary algorithms.

                 In this article, we present several new bounds for
                 different single and multi-objective algorithms on the
                 sorting problem, a problem that typically lacks
                 independent and additive fitness structures.",
  notes =        "WCCI2014",

Genetic Programming entries for Markus Wagner Frank Neumann