Controle de la croissance de la taille des individus en programmation genetique

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@PhdThesis{Gardner:thesis,
  author =       "Marc-Andre Gardner",
  title =        "Controle de la croissance de la taille des individus
                 en programmation genetique",
  school =       "Universite Laval",
  year =         "2014",
  address =      "Quebec, Canada",
  keywords =     "genetic algorithms, genetic programming, bloat",
  URL =          "http://theses.ulaval.ca/archimede/?wicket:interface=:2::::",
  URL =          "http://www.theses.ulaval.ca/2014/31215/31215.pdf",
  size =         "167 pages",
  abstract =     "Genetic programming is a hyperheuristic optimization
                 approach that has been applied to a wide range of
                 problems involving symbolic representations or complex
                 data structures. However, the method can be severely
                 hindered by the increased computational resources
                 required and premature convergence caused by
                 uncontrolled code growth. We introduce HARM-GP, a novel
                 operator equalization approach that adaptively shapes
                 the genotype size distribution of individuals in order
                 to effectively control code growth. Its probabilistic
                 nature minimizes the overhead on the evolutionary
                 process while its generic formulation allows this
                 approach to remain independent of the problem and
                 genetic operators used. Comparative results are
                 provided over twelve problems with different dynamics,
                 and over nine other algorithms taken from the
                 literature. They show that HARM-GP is excellent at
                 controlling code growth while maintaining good overall
                 performances. Results also demonstrate the
                 effectiveness of HARM-GP at limiting overtraining and
                 overfitting in real-world supervised learning
                 problems.",
  resume =       "La programmation genetique (GP) est une
                 hyperheuristique d'optimisation ayant ete appliquee
                 avec succes a un large eventail de problemes.
                 Cependant, son interet est souvent considerablement
                 diminue du fait de son utilisation elevee en ressources
                 de calcul et de sa convergence laborieuse. Ces
                 problemes sont causes par une croissance immoderee de
                 la taille des solutions et par l'apparition de
                 structures inutiles dans celles-ci. Dans ce memoire,
                 nous presentons HARM-GP, une nouvelle approche
                 resolvant en grande partie ces problemes en permettant
                 une adaptation dynamique de la distribution des tailles
                 des solutions, tout en minimisant l'effort de calcul
                 requis. Les performances de HARM-GP ont ete testees sur
                 un ensemble de douze problemes et comparees avec celles
                 de neuf techniques issues de la litterature. Les
                 resultats montrent que HARM-GP excelle au controle de
                 la croissance des arbres et du surapprentissage, tout
                 en maintenant de bonnes performances sur les autres
                 aspects",
  notes =        "In French

                 supervisor: Christian Gagne",
}

Genetic Programming entries for Marc-Andre Gardner

Citations