Evolving a Nelder-Mead Algorithm for Optimization with Genetic Programming

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

  author =       "Iztok Fajfar and Janez Puhan and Arpad Burmen",
  title =        "Evolving a Nelder-Mead Algorithm for Optimization with
                 Genetic Programming",
  journal =      "Evolutionary Computation",
  year =         "2017",
  volume =       "25",
  number =       "3",
  pages =        "351–-373",
  month =        "Fall",
  keywords =     "genetic algorithms, genetic programming",
  ISSN =         "1063-6560",
  DOI =          "doi:10.1162/EVCO_a_00174",
  size =         "23 page",
  abstract =     "We use genetic programming to evolve a direct search
                 optimization algorithm, similar to that of the standard
                 downhill simplex optimization method proposed by Nelder
                 and Mead (1965). In training process, we use several
                 10-dimensional quadratic functions with randomly
                 displaced parameters and different randomly generated
                 starting simplices. The genetically obtained
                 optimization algorithm shows overall better performance
                 than the original Nelder-Mead method on a standard set
                 of test functions. We observe that many parts of the
                 genetically produced algorithm are seldom or never
                 executed, which allows us to greatly simplify the
                 algorithm by removing the redundant parts. The
                 resulting algorithm turns out to be considerably
                 simpler than the original Nelder-Mead method while
                 still performing better than the original method.",

Genetic Programming entries for Iztok Fajfar Janez Puhan Arpad Burmen