Semantic-based Local Search in Multiobjective Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4448

  author =       "Tiantian Dou and Peter Rockett",
  title =        "Semantic-based Local Search in Multiobjective Genetic
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference Companion",
  series =       "GECCO '17",
  year =         "2017",
  isbn13 =       "978-1-4503-4939-0",
  address =      "Berlin, Germany",
  pages =        "225--226",
  size =         "2 pages",
  URL =          "",
  DOI =          "doi:10.1145/3067695.3076015",
  acmid =        "3076015",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  keywords =     "genetic algorithms, genetic programming, local search,
                 model selection, multiobjective optimization,
                 semantic-based genetic programming",
  month =        "15-19 " # jul,
  abstract =     "We report a series of experiments within a
                 multiobjective genetic programming (GP) framework using
                 semantic-based local GP search. We have made comparison
                 with the Random Desired Operator (RDO) of Pawlak et al.
                 and find that a standard generational GP followed by a
                 carefully-designed single-objective GP implementing
                 semantic-based local search yields results
                 statistically comparable to those obtained with the RDO
                 operator. The trees obtained with our GP-based local
                 search technique are, however, around half the size of
                 the trees resulting from the use of the RDO.",
  notes =        "Also known as \cite{Dou:2017:SLS:3067695.3076015}
                 GECCO-2017 A Recombination of the 26th International
                 Conference on Genetic Algorithms (ICGA-2017) and the
                 22nd Annual Genetic Programming Conference (GP-2017)",

Genetic Programming entries for Tiantian Dou Peter I Rockett