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Evaluations of Genetic Programming and Neural Networks Techniques for Nuclear Material Identification
Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2000), стр. 590--596. Las Vegas, Nevada, USA, Morgan Kaufmann, (10-12 July 2000)
Аннотация
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.
