APPLICATION OF GENETIC ALGORITHM IN NEUTRON SPECTRUM UNFOLDING

Bonner Spheres neutron spectrometer has been widely applied as neutron dosimeter, however the derivation of neutron energy spectrum from its measurement data is still a significantly difficult task. This unfolding problem is proved to be ill-posed, under-determined and have no exact solution. Two major require of the unfolding methods are accuracy and stability. Most unfolding methods try to search the solution that best fit the measurement data and the response function. As a universal optimization tool Genetic Algorithm shows its potential to solve this kind of problem. Through gene operation of every generation, GA could find the global optimal among the searching space. A new fitness function which contains a distance part and a penalty part was constructed in this research. The distance part is the square distance between the individual and the measurement data. The penalty part which is a function associated with the continuity of individual is used to avoid intensively change of unfolded data. Five classical neutron spectra were chosen as benchmark input spectra. The product of the benchmark spectra and the response function played as input measurement data of the unfolding program. The unfolded results showed good agreement with the real ones. The measurement data could be well reproduced by the unfolded results though the results had some difference with the real spectra.