Bayesian Training of Neural Networks Using Genetic Programming

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  author =       "Tshilidzi Marwala",
  title =        "Bayesian Training of Neural Networks Using Genetic
  journal =      "Pattern Recognition Letters",
  year =         "2007",
  volume =       "28",
  number =       "12",
  pages =        "1452--1458",
  keywords =     "genetic algorithms, genetic programming, Bayesian
                 framework, Evolutionary programming, Neural networks",
  ISSN =         "0167-8655",
  URL =          "",
  DOI =          "doi:10.1016/j.patrec.2007.03.004",
  abstract =     "Bayesian neural network trained using Markov chain
                 Monte Carlo (MCMC) and genetic programming in binary
                 space within Metropolis framework is proposed. The
                 algorithm proposed here has the ability to learn using
                 samples obtained from previous steps merged using
                 concepts of natural evolution which include mutation,
                 crossover and reproduction. The reproduction function
                 is the Metropolis framework and binary mutation as well
                 as simple crossover, are also used. The proposed
                 algorithm is tested on simulated function, an
                 artificial taster using measured data as well as
                 condition monitoring of structures and the results are
                 compared to those of a classical MCMC method. Results
                 confirm that Bayesian neural networks trained using
                 genetic programming offers better performance and
                 efficiency than the classical approach.",
  notes =        "Also known as \cite{Marwala20071452}",

Genetic Programming entries for Tshilidzi Marwala