Fighting Bloat with Nonparametric Parsimony Pressure

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Sean Luke and Liviu Panait",
  title =        "Fighting Bloat with Nonparametric Parsimony Pressure",
  booktitle =    "Parallel Problem Solving from Nature - PPSN VII",
  address =      "Granada, Spain",
  month =        "7-11 " # sep,
  pages =        "411--421",
  year =         "2002",
  editor =       "Juan J. Merelo-Guervos and Panagiotis Adamidis and 
                 Hans-Georg Beyer and Jose-Luis Fernandez-Villacanas and 
                 Hans-Paul Schwefel",
  number =       "2439",
  series =       "Lecture Notes in Computer Science, LNCS",
  publisher =    "Springer-Verlag",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-44139-5",
  URL =          "",
  URL =          "",
  DOI =          "doi:10.1007/3-540-45712-7_40",
  abstract =     "Many forms of parsimony pressure are parametric, that
                 is final fitness is a parametric model of the actual
                 size and raw fitness values. The problem with
                 parametric techniques is that they are hard to tune to
                 prevent size from dominating fitness late in the
                 evolutionary run, or to compensate for
                 problem-dependent nonlinearities in the raw fitness
                 function. In this paper we briefly discuss existing
                 bloat-control techniques, then introduce two new kinds
                 of non-parametric parsimony pressure, Direct and
                 Proportional Tournament. As their names suggest, these
                 techniques are based on simple modifications of
                 tournament selection to consider both size and fitness,
                 but not together as a combined parametric equation. We
                 compare the techniques against, and in combination
                 with, the most popular genetic programming
                 bloat-control technique, Koza-style depth limiting, and
                 show that they are effective in limiting size while
                 still maintaining good best-fitness-of-run results.",

Genetic Programming entries for Sean Luke Liviu Panait