Dynamic Limits for Bloat Control: Variations on Size and Depth

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

@InProceedings{Silva:DLf:gecco2004,
  author =       "Sara Silva and Ernesto Costa",
  title =        "Dynamic Limits for Bloat Control: Variations on Size
                 and Depth",
  booktitle =    "Genetic and Evolutionary Computation -- GECCO-2004,
                 Part II",
  year =         "2004",
  editor =       "Kalyanmoy Deb and Riccardo Poli and 
                 Wolfgang Banzhaf and Hans-Georg Beyer and Edmund Burke and 
                 Paul Darwen and Dipankar Dasgupta and Dario Floreano and 
                 James Foster and Mark Harman and Owen Holland and 
                 Pier Luca Lanzi and Lee Spector and Andrea Tettamanzi and 
                 Dirk Thierens and Andy Tyrrell",
  series =       "Lecture Notes in Computer Science",
  pages =        "666--677",
  address =      "Seattle, WA, USA",
  publisher_address = "Heidelberg",
  month =        "26-30 " # jun,
  organisation = "ISGEC",
  publisher =    "Springer-Verlag",
  volume =       "3103",
  ISBN =         "3-540-22343-6",
  ISSN =         "0302-9743",
  URL =          "http://cisuc.dei.uc.pt/ecos/dlfile.php?fn=714_pub_31030666.pdf&idp=714",
  DOI =          "doi:10.1007/b98645",
  size =         "12",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "We present two important variations on a recently
                 successful bloat control technique, Dynamic Maximum
                 Tree Depth, intended at further improving the results
                 and extending the idea to non tree-based GP. Dynamic
                 Maximum Tree Depth introduces a dynamic limit on the
                 depth of the trees allowed into the population,
                 initially set with a low value but increased whenever
                 needed to accommodate a new best individual that would
                 otherwise break the limit. The first variation to this
                 idea is the Heavy Dynamic Limit that, unlike the
                 original one, may fall again to a lower value after it
                 has been raised, in case the new best individual allows
                 it. The second variation is the Dynamic Size Limit,
                 where size is the number of nodes, instead and
                 regardless of depth. The variations were tested in two
                 problems, Symbolic Regression and Parity, and the
                 results show that the heavy limit performs generally
                 better than the original technique, but the dynamic
                 limit on size fails in the Parity problem. The possible
                 reasons for success and failure are discussed.",
  notes =        "GECCO-2004 A joint meeting of the thirteenth
                 international conference on genetic algorithms
                 (ICGA-2004) and the ninth annual genetic programming
                 conference (GP-2004)",
}

Genetic Programming entries for Sara Silva Ernesto Costa

Citations