Fitness Causes Bloat: Mutation

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

@InProceedings{Langdon:1998:bloatMUTegp,
  author =       "W. B. Langdon and R. Poli",
  title =        "Fitness Causes Bloat: Mutation",
  booktitle =    "Proceedings of the First European Workshop on Genetic
                 Programming",
  year =         "1998",
  editor =       "Wolfgang Banzhaf and Riccardo Poli and 
                 Marc Schoenauer and Terence C. Fogarty",
  volume =       "1391",
  series =       "LNCS",
  pages =        "37--48",
  address =      "Paris",
  publisher_address = "Berlin",
  month =        "14-15 " # apr,
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-64360-5",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.euro98_bloatm.pdf",
  URL =          "http://www.cs.ucl.ac.uk/staff/W.Langdon/ftp/papers/WBL.euro98_bloatm.ps.gz",
  DOI =          "doi:10.1007/BFb0055926",
  size =         "12 pages",
  abstract =     "The problem of evolving, using mutation, an artificial
                 ant to follow the Santa Fe trail is used to study the
                 well known genetic programming feature of growth in
                 solution length. Known variously as 'bloat', 'fluff'
                 and increasing 'structural complexity', this is often
                 described in terms of increasing 'redundancy' in the
                 code caused by 'introns'.

                 Comparison between runs with and without fitness
                 selection pressure, backed by Price's Theorem, shows
                 the tendency for solutions to grow in size is caused by
                 fitness based selection. We argue that such growth is
                 inherent in using a fixed evaluation function with a
                 discrete but variable length representation. With
                 simple static evaluation search converges to mainly
                 finding trial solutions with the same fitness as
                 existing trial solutions. In general variable length
                 allows many more long representations of a given
                 solution than short ones. Thus in search (without a
                 length bias) we expect longer representations to occur
                 more often and so representation length to tend to
                 increase. I.e. fitness based selection leads to
                 bloat.",
  notes =        "EuroGP'98 Based on \cite{Langdon:1997:bloatMUT}",
  affiliation =  "University of Birmingham School of Computer Science
                 B15 2TT Birmingham UK B15 2TT Birmingham UK",
}

Genetic Programming entries for William B Langdon Riccardo Poli

Citations