Semantic Similarity based Crossover in GP: The case for Real-valued Function Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InProceedings{Nguyen:2009:EA,
  author =       "Nguyen Quang Uy and Michael O'Neill and 
                 Xuan Hoai Nguyen and Bob McKay and Edgar Galvan Lopez",
  title =        "Semantic Similarity based Crossover in {GP}: The case
                 for Real-valued Function Regression",
  booktitle =    "9th International Conference, Evolution Artificielle,
                 EA 2009",
  year =         "2009",
  editor =       "Pierre Collet and Nicolas Monmarche and 
                 Pierrick Legrand and Marc Schoenauer and Evelyne Lutton",
  volume =       "5975",
  series =       "Lecture Notes in Computer Science",
  pages =        "170--181",
  address =      "Strasbourg, France",
  month =        oct # " 26-28",
  publisher =    "Springer",
  note =         "Revised Selected Papers",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-14155-3",
  doi =          "doi:10.1007/978-3-642-14156-0",
  size =         "12 pages",
  abstract =     "In this paper we propose a new method for implementing
                 the crossover operator in Genetic Programming (GP)
                 called Semantic Similarity based Crossover (SSC). This
                 new operator is inspired by Semantic Aware Crossover
                 (SAC) [20]. SSC extends SAC by adding semantics to
                 control the change of the semantics of the individuals
                 during the evolutionary process. The new crossover
                 operator is then tested on a family of symbolic
                 regression problems and compared with SAC as well as
                 Standard Crossover (SC). The results from the
                 experiments show that the change of the semantics
                 (fitness) in the new SSC is smoother compared to SAC
                 and SC. This leads to performance improvement in terms
                 of percentage of successful runs and mean best
                 fitness.",
  notes =        "EA'09 Published 2010

                 See use of family name (Nguyen) in PhD thesis
                 \cite{Quang_Uy_Nguyen:thesis} Firstnames: Uy
                 Quang

                 Natural Computing Research & Applications Group,
                 University College Dublin, Ireland

                 School of Computer Science and Engineering, Seoul
                 National University, Korea",
}

Genetic Programming entries for Quang Uy Nguyen Michael O'Neill Nguyen Xuan Hoai R I (Bob) McKay Edgar Galvan Lopez