Parent Selection Pressure Auto-tuning for Tournament Selection in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Huayang Xie and Mengjie Zhang",
  title =        "Parent Selection Pressure Auto-tuning for Tournament
                 Selection in Genetic Programming",
  journal =      "IEEE Transactions on Evolutionary Computation",
  year =         "2013",
  volume =       "17",
  number =       "1",
  pages =        "1--19",
  month =        feb,
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1089-778X",
  DOI =          "doi:10.1109/TEVC.2011.2182652",
  abstract =     "Selection pressure restraints the selection of
                 individuals from the current population to produce a
                 new population in the next generation. It gives
                 individuals of higher quality a higher probability of
                 being used to create the next generation so that
                 Evolutionary Algorithms (EAs) can focus on promising
                 regions in the search space. An evolutionary learning
                 process is dynamic and requires different selection
                 pressures at different learning stages in order to
                 speed up convergence or avoid local optima. Therefore,
                 it desires selection mechanisms being able to
                 automatically tune selection pressure during evolution.
                 Tournament selection is a popular selection method in
                 EAs, especially genetic algorithms and Genetic
                 Programming (GP). This paper focuses on tournament
                 selection and shows that the standard tournament
                 selection scheme is unaware of the dynamics in the
                 evolutionary process and that the standard tournament
                 selection scheme is unable to tune selection pressure
                 automatically. This paper then presents a novel
                 approach which integrates the knowledge of the Fitness
                 Rank Distribution (FRD) of a population into tournament
                 selection. Through mathematical modelling, simulations
                 and experimental study in GP, this paper shows that the
                 new approach is effective and using the knowledge of
                 FRD is a promising way to modify the standard
                 tournament selection method for tuning the selection
                 pressure dynamically and automatically along
  notes =        "also known as \cite{6151120}",

Genetic Programming entries for Huayang Jason Xie Mengjie Zhang