改进随机函数局限性样本取数优化算法

Sample Fetching Optimization Algorithm of Improved Random Function Limitation

为了解决各类考试系统中随机抽取试题时存在试题重复和试题命中率不均匀的现象,提出一种改进随机取数局限性的样本取数优化算法。通过引入样本概念,从试题库中采集题号产生题号样本,利用随机函数产生预选样本,由预选样本在题号样本中映射出所对应的题号,形成试卷草样,经过排序形成试卷正样,完成试卷生成工作。采用样本取数优化算法具有较好的区间收敛性,试题库中试题的命中率高,接近平均值。该算法应用于试题库抽取试题具有速度快、抽取时数据量轻,组卷速度快。

In order to solve various types of test systems that existed when a random sample test questions and the questions to repeat the phenomenon of uneven percentage of hits, a number of limitations to improve the random sample taken from a number of optimization algorithms. Through introduction of the sample concept of library collection from the examination title number of the topic number sample, the use of a random function of pre- selected samples, samples from the pre- selection samples in the title of its corresponding mapping out the title number, the examination paper outline sorting through formation of the examination paper type, the completion of paper work generated. Using samples from a number of optimization algorithm which has a good range of convergence,test questions library of hit rate,close to average. This algorithm is applied in extraction of test question which has high speed, light extraction of data quantity, the speed of generated papers is quick.