Evaluations of Genetic Programming and Neural Networks Techniques for Nuclear Material Identification

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Segovia:2000:GECCO,
  author =       "Sara Pozzi and Javier Segovia",
  title =        "Evaluations of Genetic Programming and Neural Networks
                 Techniques for Nuclear Material Identification",
  pages =        "590--596",
  year =         "2000",
  publisher =    "Morgan Kaufmann",
  booktitle =    "Proceedings of the Genetic and Evolutionary
                 Computation Conference (GECCO-2000)",
  editor =       "Darrell Whitley and David Goldberg and 
                 Erick Cantu-Paz and Lee Spector and Ian Parmee and Hans-Georg Beyer",
  address =      "Las Vegas, Nevada, USA",
  publisher_address = "San Francisco, CA 94104, USA",
  month =        "10-12 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "1-55860-708-0",
  URL =          "http://www.cs.bham.ac.uk/~wbl/biblio/gecco2000/RW225.pdf",
  abstract =     "Nuclear materials safeguard efforts necessitate the
                 use of non-destructive methods to determine the
                 attributes of fissile samples enclosed in special,
                 non-accessible containers. To this end, a large variety
                 of methods has been developed. Usually, a given set of
                 statistics of the stochastic neutron-photon coupled
                 field, such as sourcedetector, detector-detector cross
                 correlation functions, and multiplicities are measured
                 over a range of known samples to develop calibration
                 algorithms. In this manner, the attributes of unknown
                 samples can be inferred by the use of the calibration
                 results.

                 The sample identification problem, in its most general
                 setting, is then to determine the relationship between
                 the observed features of the measurement and the sample
                 attributes and to combine them for the construction of
                 an optimal identification algorithm. The goal of this
                 paper is to compare a combination of genetic algorithms
                 and neural networks (NN) with genetic programming (GP)
                 for this purpose. To this end, the time-dependent
                 MCNP-DSP Monte Carlo code has been used to simulate the
                 neutron-photon interrogation of sets of uranium metal
                 samples by a 252Cf-source. The resulting sets of
                 source-detector correlation functions, R12(? ) as a
                 function of the time delay, ? , served as a data-base
                 for the training and testing of the algorithms.",
  notes =        "A joint meeting of the ninth International Conference
                 on Genetic Algorithms (ICGA-2000) and the fifth Annual
                 Genetic Programming Conference (GP-2000) Part of
                 \cite{whitley:2000:GECCO}",
}

Genetic Programming entries for Sara A Pozzi Javier Segovia

Citations