Theory-Laden Design of Mutation-Based Geometric Semantic Genetic Programming for Learning Classification Trees

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

@InProceedings{Mambrini:2013:CEC,
  article_id =   "1697",
  author =       "Andrea Mambrini and Luca Manzoni and 
                 Alberto Moraglio",
  title =        "Theory-Laden Design of Mutation-Based Geometric
                 Semantic Genetic Programming for Learning
                 Classification Trees",
  booktitle =    "2013 IEEE Conference on Evolutionary Computation",
  volume =       "1",
  year =         "2013",
  month =        jun # " 20-23",
  editor =       "Luis Gerardo {de la Fraga}",
  pages =        "416--423",
  address =      "Cancun, Mexico",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4799-0453-2",
  DOI =          "doi:10.1109/CEC.2013.6557599",
  size =         "8 pages",
  abstract =     "Geometric Semantic Genetic Programming (GSGP) is a
                 recently introduced framework to design domain-specific
                 search operators for Genetic Programming (GP) to search
                 directly the semantic space of functions. The fitness
                 landscape seen by GSGP is always - for any domain and
                 for any problem - unimodal with a constant slope by
                 construction. This makes the search for the optimum
                 much easier than for traditional GP, and it opens the
                 way to analyse theoretically in a easy manner the
                 optimisation time of GSGP in a general setting. We
                 design and analyse a mutation-based GSGP for the class
                 of all classification tree learning problems, which is
                 a classic GP application domain.",
  notes =        "CEC 2013 - A joint meeting of the IEEE, the EPS and
                 the IET.",
}

Genetic Programming entries for Andrea Mambrini Luca Manzoni Alberto Moraglio

Citations