遗传算法
计算机科学
文化算法
适应度函数
领域(数学)
考试(生物学)
基于群体的增量学习
人口
功能(生物学)
遗传代表性
人工智能
算法
机器学习
数学
社会学
人口学
古生物学
生物
进化生物学
纯数学
出处
期刊:Journal of physics
[IOP Publishing]
日期:2021-02-01
卷期号:1744 (3): 032203-032203
被引量:2
标识
DOI:10.1088/1742-6596/1744/3/032203
摘要
Abstract Intelligent test paper generation belongs to an important research topic in the field of computer-aided education. Among them, Genetic Algorithm (GA), as an algorithm with high efficiency and performance, is widely used in the intelligent test paper generating system. But generally speaking, traditional GA always has some problems, such as local optimal solution and prematurity. The traditional genetic algorithm is improved in the aspects of the initial population, fitness function and some genetic operators to make it more in line with the requirements of intelligent paper generation, improving the efficiency and success ratio.
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