Semantics based Mutation in Genetic Programming: The case for Real-valued Symbolic Regression

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Nguyen:2009:MENDEL,
  author =       "Quang Uy Nguyen and Xuan Hoai Nguyen and 
                 Michael O'Neill",
  title =        "Semantics based Mutation in Genetic Programming: The
                 case for Real-valued Symbolic Regression",
  booktitle =    "15th International Conference on Soft Computing,
                 Mendel'09",
  year =         "2009",
  editor =       "R. Matousek and L. Nolle",
  pages =        "73--91",
  address =      "Brno, Czech Republic",
  month =        jun # " 24-26",
  email =        "quanguyhn@yahoo.com",
  keywords =     "genetic algorithms, genetic programming, Semantics,
                 Mutation Operator, Symbolic Regression",
  isbn13 =       "978-80-214-3884-2",
  URL =          "http://ncra.ucd.ie/papers/mendel2009SSM.pdf",
  size =         "8 pages",
  abstract =     "In this paper we propose two new methods for
                 implementing the mutation operator in Genetic
                 Programming called Semantic Aware Mutation (SAM) and
                 Semantic Similarity based Mutation (SSM). SAM is
                 inspired by our previous work on a semantics based
                 crossover called Semantic Aware Crossover (SAC) [19]
                 and SSM is an extension of SAM by adding more control
                 on the change of semantics of the subtrees involved in
                 mutation operation. We apply these two new mutation
                 operators to a class of real-valued symbolic regression
                 problems and compare them with the Standard Mutation
                 (SM) of Koza [13]. The results from the experiments
                 show that while SAM does not help to improve the
                 performance of Genetic Programming, SSM helps to
                 significantly enhance Genetic Programming performance
                 on the problems tried. The experiment results also show
                 that the change of the semantics (fitness) in SSM is
                 smoother than ones of both SAM and SM. This, we argue
                 that is the main reason to the significant performance
                 improvement of SSM over SAM and SC.",
  notes =        "http://www.mendel-conference.org/ ID09051 Also in
                 electronic form ISSN 1803-3814",
}

Genetic Programming entries for Quang Uy Nguyen Nguyen Xuan Hoai Michael O'Neill

Citations