A Comparison of Bloat Control Methods for Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Sean Luke and Liviu Panait",
  title =        "A Comparison of Bloat Control Methods for Genetic
  journal =      "Evolutionary Computation",
  year =         "2006",
  volume =       "14",
  number =       "3",
  pages =        "309--344",
  month =        "Fall",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/evco.2006.14.3.309",
  oai =          "oai:CiteSeerX.psu:",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=",
  URL =          "http://cognet.mit.edu/system/cogfiles/journalpdfs/evco.2006.14.3.309.pdf",
  abstract =     "Genetic programming has highlighted the problem of
                 bloat, the uncontrolled growth of the average size of
                 an individual in the population. The most common
                 approach to dealing with bloat in tree-based genetic
                 programming individuals is to limit their maximal
                 allowed depth. An alternative to depth limiting is to
                 punish individuals in some way based on excess size,
                 and our experiments have shown that the combination of
                 depth limiting with such a punitive method is generally
                 more effective than either alone. Which such
                 combinations are most effective at reducing bloat? In
                 this article we augment depth limiting with nine bloat
                 control methods and compare them with one another.
                 These methods are chosen from past literature and from
                 techniques of our own devising. testing with four
                 genetic programming problems, we identify where each
                 bloat control method performs well on a per-problem
                 basis, and under what settings various methods are
                 effective independent of problem. We report on the
                 results of these tests, and discover an unexpected
                 winner in the cross-platform category.",

Genetic Programming entries for Sean Luke Liviu Panait