Evolving Evolutionary Algorithms Using Multi Expression Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Mihai Oltean and Crina Grosan",
  title =        "Evolving Evolutionary Algorithms Using Multi
                 Expression Programming",
  booktitle =    "Advances in Artificial Life. 7th European Conference
                 on Artificial Life",
  year =         "2003",
  editor =       "Wolfgang Banzhaf and Thomas Christaller and 
                 Peter Dittrich and Jan T. Kim and Jens Ziegler",
  number =       "2801",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "651--658",
  address =      "Dortmund, Germany",
  month =        "14-17 " # sep,
  publisher =    "Springer",
  email =        "moltean@cs.ubbcluj.ro",
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-540-20057-6",
  URL =          "http://www.mep.cs.ubbcluj.ro/oltean_ecal2003.pdf",
  DOI =          "doi:10.1007/b12035",
  size =         "8 pages",
  abstract =     "Finding the optimal parameter setting (i.e. the
                 optimal population size, the optimal mutation
                 probability, the optimal evolutionary model etc) for an
                 Evolutionary Algorithm (EA) is a difficult task.
                 Instead of evolving only the parameters of the
                 algorithm we will evolve an entire EA capable of
                 solving a particular problem. For this purpose the
                 Multi Expression Programming (MEP) technique is used.
                 Each MEP chromosome will encode multiple EAs. An
                 nongenerational EA for function optimisation is evolved
                 in this paper. Numerical experiments show the
                 effectiveness of this approach.",
  notes =        "ECAL-2003 Also available at www.eea.cs.ubbcluj.ro",

Genetic Programming entries for Mihai Oltean Crina Grosan