Controlling Individuals Growth in Semantic Genetic Programming through Elitist Replacement

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@Article{Castelli:2016:CIN,
  author =       "Mauro Castelli and Leonardo Vanneschi and 
                 Ales Popovic",
  title =        "Controlling Individuals Growth in Semantic Genetic
                 Programming through Elitist Replacement",
  journal =      "Computational Intelligence and Neuroscience",
  year =         "2016",
  pages =        "Article ID 8326760",
  keywords =     "genetic algorithms, genetic programming",
  publisher =    "Hindawi Publishing Corporation",
  bibsource =    "OAI-PMH server at www.ncbi.nlm.nih.gov",
  identifier =   "/pmc/articles/PMC4707023/",
  language =     "en",
  oai =          "oai:pubmedcentral.nih.gov:4707023",
  rights =       "Copyright 2016 Mauro Castelli et al.; This is an open
                 access article distributed under the Creative Commons
                 Attribution License, which permits unrestricted use,
                 distribution, and reproduction in any medium, provided
                 the original work is properly cited.",
  URL =          "http://dx.doi.org/10.1155/2016/8326760",
  URL =          "http://downloads.hindawi.com/journals/cin/2016/8326760.pdf",
  size =         "12 pages",
  abstract =     "In 2012, Moraglio and coauthors introduced new genetic
                 operators for Genetic Programming, called geometric
                 semantic genetic operators. They have the very
                 interesting advantage of inducing a unimodal error
                 surface for any supervised learning problem. At the
                 same time, they have the important drawback of
                 generating very large data models that are usually very
                 hard to understand and interpret. The objective of this
                 work is to alleviate this drawback, still maintaining
                 the advantage. More in particular, we propose an
                 elitist version of geometric semantic operators, in
                 which offspring are accepted in the new population only
                 if they have better fitness than their parents. We
                 present experimental evidence, on five complex
                 real-life test problems, that this simple idea allows
                 us to obtain results of a comparable quality (in terms
                 of fitness), but with much smaller data models,
                 compared to the standard geometric semantic operators.
                 In the final part of the paper, we also explain the
                 reason why we consider this a significant improvement,
                 showing that the proposed elitist operators generate
                 manageable models, while the models generated by the
                 standard operators are so large in size that they can
                 be considered unmanageable.",
}

Genetic Programming entries for Mauro Castelli Leonardo Vanneschi Ales Popovic

Citations