A Comparison of Selection Schemes Used in Genetic Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.4333

  author =       "Tobias Blickle and Lothar Thiele",
  title =        "A Comparison of Selection Schemes Used in Genetic
  institution =  "TIK Institut fur Technische Informatik und
                 Kommunikationsnetze, Computer Engineering and Networks
                 Laboratory, ETH, Swiss Federal Institute of
  year =         "1995",
  type =         "TIK-Report",
  number =       "11",
  edition =      "2",
  address =      "Gloriastrasse 35, 8092 Zurich, Switzerland",
  month =        dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "http://www.handshake.de/user/blickle/publications/TIK-Report11abstract.html",
  URL =          "http://www.handshake.de/user/blickle/publications/tik-report11_v2.ps",
  abstract =     "

                 Genetic Algorithms are a common probabilistic
                 optimization method based on the model of natural
                 evolution. One important operator in these algorithms
                 is the selection scheme for which a new description
                 model is introduced in this paper. With this a
                 mathematical analysis of tournament selection,
                 truncation selection, linear and exponential ranking
                 selection and proportional selection is carried out
                 that allows an exact prediction of the fitness values
                 after selection. The further analysis derives the
                 selection intensity, selection variance, and the loss
                 of diversity for all selection schemes. For completion
                 a pseudo- code formulation of each method is included.
                 The selection schemes are compared and evaluated
                 according to their properties leading to an unified
                 view of these different selection schemes. Furthermore
                 the correspondence of binary tournament selection and
                 ranking selection in the expected fitness distribution
                 is proven.",
  notes =        "

                 Of special interest for the GP community may be the
                 fact that in this report three analytic approximation
                 formulas are obtained using GP for symbolic regression.
                 The method is described in appendix A of the

                 Second (extended and corrected) edition available via
                 www and ftp Dec 1995

  size =         "65 pages",

Genetic Programming entries for Tobias Blickle Lothar Thiele