Dynamic Population Variation in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Kouchakpour2008,
  author =       "Peyman Kouchakpour and Anthony Zaknich and 
                 Thomas Braunl",
  title =        "Dynamic Population Variation in Genetic Programming",
  journal =      "Information Sciences",
  year =         "2009",
  volume =       "179",
  number =       "8",
  pages =        "1078--1091",
  month =        "29 " # mar,
  keywords =     "genetic algorithms, genetic programming, Computational
                 Effort, Average Number of Evaluations, Convergence,
                 Diversity, Population Variation, Dynamic
                 PopulationVariation",
  ISSN =         "0020-0255",
  URL =          "http://www.sciencedirect.com/science/article/B6V0C-4V7647M-2/2/7260de43f34e6b9e878cb068373a639f",
  DOI =          "doi:10.1016/j.ins.2008.12.009",
  abstract =     "Three innovations are proposed for dynamically varying
                 the population size during the run of the genetic
                 programming (GP) system. These are related to what is
                 called Dynamic Population Variation (DPV), where the
                 size of the population is dynamically varied using a
                 heuristic feedback mechanism during the execution of
                 the GP with the aim of reducing the computational
                 effort compared with Standard Genetic Programming
                 (SGP). Firstly, previously developed population
                 variation pivot functions are controlled by four newly
                 proposed characteristic measures. Secondly, a new
                 gradient based pivot function is added to this dynamic
                 population variation method in conjunction with the
                 four proposed measures. Thirdly, a formula for
                 population variations that is independent of special
                 constants is introduced and evaluated. The efficacy of
                 these innovations is examined using a comprehensive
                 range of standard representative problems. It is shown
                 that the new ideas do have the capacity to provide
                 solutions at a lower computational cost compared with
                 standard genetic programming and previously reported
                 algorithms such as the plague operator and the static
                 population variation schemes previously introduced by
                 the authors.",
}

Genetic Programming entries for Peyman Kouchakpour Anthony Zaknich Thomas Braunl

Citations