Preliminary Study of Bloat in Genetic Programming with Behavior-Based Search

Created by W.Langdon from gp-bibliography.bib Revision:1.3949

@InProceedings{Trujillo:2013:EVOLVE,
  author =       "Leonardo Trujillo and Enrique Naredo and 
                 Yuliana Martinez",
  title =        "Preliminary Study of Bloat in Genetic Programming with
                 Behavior-Based Search",
  booktitle =    "EVOLVE - A Bridge between Probability, Set Oriented
                 Numerics, and Evolutionary Computation IV",
  year =         "2013",
  editor =       "Michael Emmerich and Andre Deutz and 
                 Oliver Schuetze and Thomas Baeck and Emilia Tantar and 
                 Alexandru-Adrian and Pierre {Del Moral} and Pierrick Legrand and 
                 Pascal Bouvry and Carlos A. Coello",
  volume =       "227",
  series =       "Advances in Intelligent Systems and Computing",
  pages =        "293--305",
  address =      "Leiden, Holland",
  month =        jul # " 10-13",
  publisher =    "Springer",
  keywords =     "genetic algorithms, genetic programming, Bloat,
                 Novelty Search",
  isbn13 =       "978-3-319-01127-1",
  DOI =          "doi:10.1007/978-3-319-01128-8_19",
  abstract =     "Bloat is one of the most interesting theoretical
                 problems in genetic programming (GP), and one of the
                 most important pragmatic limitations in the development
                 of real-world GP solutions. Over the years, many
                 theories regarding the causes of bloat have been
                 proposed and a variety of bloat control methods have
                 been developed. It seems that one of the underlying
                 causes of bloat is the search for fitness; as the
                 fitness-causes-bloat theory states, selective bias
                 towards fitness seems to unavoidably lead the search
                 towards programs with a large size. Intuitively,
                 however, abandoning fitness does not appear to be an
                 option. This paper, studies a GP system that does not
                 require an explicit fitness function, instead it relies
                 on behavior-based search, where programs are described
                 by the behavior they exhibit and selective pressure is
                 biased towards unique behaviours using the novelty
                 search algorithm. Initial results are encouraging, the
                 average program size of the evolving population does
                 not increase with novelty search; i.e., bloat is
                 avoided by focusing on novelty instead of quality.",
}

Genetic Programming entries for Leonardo Trujillo Enrique Naredo Yuliana Martinez

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