Controlling code growth by dynamically shaping the genotype size distribution

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

@Article{Gardner:2015:GPEM,
  author =       "Marc-Andre Gardner and Christian Gagne and 
                 Marc Parizeau",
  title =        "Controlling code growth by dynamically shaping the
                 genotype size distribution",
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2015",
  volume =       "16",
  number =       "4",
  pages =        "455--498",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming, Bloat
                 control, Monte Carlo methods",
  ISSN =         "1389-2576",
  DOI =          "doi:10.1007/s10710-015-9242-8",
  size =         "44 pages",
  abstract =     "Genetic programming is a hyperheuristic optimisation
                 approach that seeks to evolve various forms of symbolic
                 computer programs, in order to solve a wide range of
                 problems. However, the approach can be severely
                 hindered by a significant computational burden and
                 stagnation of the evolution caused by uncontrolled code
                 growth. This paper introduces HARM-GP, a novel operator
                 equalisation method that conducts an adaptive shaping
                 of the genotype size distribution of individuals in
                 order to effectively control code growth. Its
                 probabilistic nature minimises the computational
                 overheads on the evolutionary process while its generic
                 formulation allows it to remain independent of both the
                 problem and the genetic variation operators.
                 Comparative results over twelve problems with different
                 dynamics, and over nine other algorithms taken from the
                 literature, show that HARM-GP is excellent at
                 controlling code growth while maintaining good overall
                 performance. Results also demonstrate the effectiveness
                 of HARM-GP at limiting overfitting in real-world
                 supervised learning problems.",
}

Genetic Programming entries for Marc-Andre Gardner Christian Gagne Marc Parizeau

Citations