Evolutionary Computation and Convergence to a Pareto Front

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "David A. {Van Veldhuizen} and Gary B. Lamont",
  title =        "Evolutionary Computation and Convergence to a Pareto
  booktitle =    "Late Breaking Papers at the Genetic Programming 1998
  year =         "1998",
  editor =       "John R. Koza",
  pages =        "221--228",
  address =      "University of Wisconsin, Madison, Wisconsin, USA",
  publisher_address = "Stanford, CA, USA",
  month =        "22-25 " # jul,
  publisher =    "Stanford University Bookstore",
  keywords =     "genetic algorithms, genetic programming, MOP, GA, ES,
                 GP, EP",
  URL =          "http://www.lania.mx/~ccoello/EMOO/vanvel2.ps.gz",
  size =         "7.1 pages",
  abstract =     "Research into solving multiobjective optimisation
                 problems (MOP) has as one of its an overall goals that
                 of developing and defining foundations of an
                 Evolutionary Computation (EC)-based MOP theory. In this
                 paper, we introduce relevant MOP concepts, and the
                 notion of Pareto optimality, in particular. Specific
                 notation is defined and theorems are presented ensuring
                 Pareto based Evolutionary Algorithm (EA)
                 implementations are clearly understood. Then, a
                 specific experiment investigating the convergence of an
                 arbitrary EA to a Pareto front is presented. This
                 experiment gives a basis for a theorem showing a
                 specific multiobjective EA statistically converges to
                 the Pareto front. We conclude by using this work to
                 justify further exploration into the theoretical
                 foundations of EC-based MOP solution methods.",
  notes =        "Matlab, GEATbx GP-98LB",

Genetic Programming entries for David A Van Veldhuizen Gary B Lamont