Evolutionary Scanning and Neural Network Optimization

Created by W.Langdon from gp-bibliography.bib Revision:1.4208

  author =       "Ivan Zelinka and Roman Senkerik and Zuzana Oplatkova",
  title =        "Evolutionary Scanning and Neural Network
  booktitle =    "19th International Conference on Database and Expert
                 Systems Application, DEXA '08",
  year =         "2008",
  month =        sep,
  pages =        "576--582",
  keywords =     "genetic algorithms, genetic programming, analytic
                 programming, differential evolution, evolutionary
                 scanning, grammatical evolution, neural network
                 optimization, neural network synthesis, self organizing
                 migrating algorithm, simulated annealing, symbolic
                 regression, neural nets, simulated annealing",
  DOI =          "doi:10.1109/DEXA.2008.84",
  ISSN =         "1529-4188",
  abstract =     "This paper deals with use of an alternative tool for
                 symbolic regression - analytic programming which is
                 able to solve various problems from the symbolic domain
                 as well as genetic programming and grammatical
                 evolution. The main tasks of analytic programming in
                 this paper, is synthesis of a neural network. In this
                 contribution main principles of analytic programming
                 are described and explained. In the second part of the
                 article is in detail described how analytic programming
                 was used for neural network synthesis. An ability to
                 create so called programs, as well as genetic
                 programming or grammatical evolution do, is shown in
                 that part. In this contribution three evolutionary
                 algorithms were used - self organizing migrating
                 algorithm, differential evolution and simulated
                 annealing. The total number of simulations was 150 and
                 results show that the first two used algorithms were
                 more successful than not so robust simulated
  notes =        "Also known as \cite{4624779}",

Genetic Programming entries for Ivan Zelinka Roman Senkerik Zuzana Oplatkova