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When evolution algorithms are used to unfold the neutron energy spectrum,fitness function design is an important fundamental work for evaluating thequality of the solution, but it has not attracted much attention. In this work,we investigated the performance of eight fitness functions attached to thegenetic algorithm (GA) and the differential evolution algorithm (DEA) used forunfolding four neutron spectra selected from the IAEA 403 report. Experimentsshow that the fitness functions with a maximum in the GA can limit the abilityof the population to percept the fitness change, but the ability can be made upin the DEA. The fitness function with a feature penalty term helps to improvethe performance of solutions, and the fitness function using the standarddeviation and the Chi-squared result shows the balance between the algorithmand the spectra. The results also show that the DEA has good potential forneutron energy spectrum unfolding. The purposes of this work are to provideevidence for structuring and modifying the fitness functions and to suggestsome genetic operations that should receive attention when using the fitnessfunction to unfold neutron spectra.

Research on Fitness Function of Two Evolution Algorithms Used for Neutron Spectrum Unfolding