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Computer Optimization of Food Nutrition Formula Based on the Consideration of Adaptive Genetic Algorithm

机译:基于自适应遗传算法的食品营养配方计算机优化

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Food nutrition is related to the national welfare and the people's livelihood, while frequent food safety incidents have tested people's lives and put them at risk again and again. With the rapid development of the Internet, computer optimization of food nutrition formula (hereinafter referred to as COFNF for short) has become an important method to solve the food nutrition problems in our country. In this paper, a kind of computer optimization method for the food nutrition formula based on the consideration of the adaptive genetic algorithm as the core is put forward. The computer optimization of the food nutrition formula is generated by the matrix decomposition based on the consideration of the heterogeneous data. In this system, the food nutrition information and the sign-in data are combined in an effective manner; and at the same time, they are merged into the same system to achieve the complementary advantages. The system that takes the complementary advantages into consideration can make more accurate prediction about the preference of the users. It is found in this study through experiments that: Compared with the other mainstream computer optimization of the food nutrition formula, the COFNF algorithm has relatively significant improvement in both the accuracy index and the recall rate index.
机译:食品营养与国民福利和民生有关,而频繁发生的食品安全事件使人们的生活受到考验,并一次又一次地使他们处于危险之中。随着互联网的飞速发展,食品营养配方的计算机优化(以下简称COFNF)已成为解决我国食品营养问题的重要手段。提出了一种以自适应遗传算法为核心的食品营养配方计算机优化方法。在考虑异质数据的基础上,通过矩阵分解生成食品营养配方的计算机优化。在该系统中,有效地结合了食品营养信息和登录数据;同时将它们合并到同一系统中以实现互补优势。考虑互补优势的系统可以对用户的偏好做出更准确的预测。通过实验研究发现:与其他主流计算机对食品营养配方的优化相比,COFNF算法在准确性指标和召回率指标上都有相对显着的提高。

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