On the Use of Semantics in Multi-Objective Genetic Programming

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

  author =       "Edgar Galvan-Lopez and Efren Mezura-Montes and 
                 Ouassim Ait Elhara and Marc Schoenauer",
  title =        "On the Use of Semantics in Multi-Objective Genetic
  booktitle =    "14th International Conference on Parallel Problem
                 Solving from Nature",
  year =         "2016",
  editor =       "Julia Handl and Emma Hart and Peter R. Lewis and 
                 Manuel Lopez-Ibanez and Gabriela Ochoa and 
                 Ben Paechter",
  volume =       "9921",
  series =       "LNCS",
  pages =        "353--363",
  address =      "Edinburgh",
  month =        "17-21 " # sep,
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-319-45823-6",
  DOI =          "doi:10.1007/978-3-319-45823-6_33",
  abstract =     "Research on semantics in Genetic Programming (GP) has
                 increased dramatically over the last number of years.
                 Results in this area clearly indicate that its use in
                 GP can considerably increase GP performance. Motivated
                 by these results, this paper investigates for the first
                 time the use of Semantics in Muti-objective GP within
                 the well-known NSGA-II algorithm. To this end, we
                 propose two forms of incorporating semantics into a
                 MOGP system. Results on challenging (highly) unbalanced
                 binary classification tasks indicate that the adoption
                 of semantics in MOGP is beneficial, in particular when
                 a semantic distance is incorporated into the core of
  notes =        "PPSN2016 http://ppsn2016.org",

Genetic Programming entries for Edgar Galvan Lopez Efren Mezura-Montes Ouassim Ait Elhara Marc Schoenauer