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

中文翻译：

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用于中子谱展开的两种演化算法的适应度函数研究

在使用进化算法展开中子能谱时，适应度函数设计是评价解质量的重要基础工作，但并未引起太多关注。在这项工作中，我们研究了遗传算法 (GA) 和用于展开从 IAEA 403 报告中选择的四个中子谱的差分进化算法 (DEA) 的八个适应度函数的性能。实验表明，GA中具有最大值的适应度函数可以限制种群感知适应度变化的能力，但这种能力可以在DEA中弥补。带有特征惩罚项的适应度函数有助于提高解的性能，使用标准差和卡方结果的适应度函数显示了算法和光谱之间的平衡。结果还表明，DEA 具有良好的中子能谱展开潜力。这项工作的目的是为构建和修改适应度函数提供证据，并提出一些在使用适应度函数展开中子谱时应引起注意的遗传操作。