多种人工智能算法的数据库技术课程自动组卷比较
计算机系统应用 ISSN 1003-3254, CODEN CSAOBN E-mail: csa@https://www.wendangku.net/doc/b61580376.html, Computer Systems & Applications,2018,27(3):210?216 [doi: 10.15888/https://www.wendangku.net/doc/b61580376.html,ki.csa.006250]https://www.wendangku.net/doc/b61580376.html, ?中国科学院软件研究所版权所有.Tel: +86-10-62661041多种人工智能算法的数据库技术课程自动组卷比较①
彭康华, 黄裕锋, 姚江梅
(广东工程职业技术学院信息工程学院, 广州 510520)
摘 要: 在线考试被广泛应用在远程教育上, 自动化组卷是在线考试的关键技术, 组卷问题即是多目标期望值的求解问题, 其往往存在多个解, 人工智能算法对于求解多目标函数有明显优势. 采用遗传算法及蚁群算法的多目标优化求解更加高效, 能更好胜任于本文数据库技术课程的自动化组卷. 在讨论人工智能算法在组卷应用基础上, 构建了组卷指标体系, 建立多目标约束数学模型, 并对多目标期望值进行优化求解. 多次实验结果论证表明, 人工智能算法的成功率最高, 平均达到98%以上(含蚁群算法100%, 遗传算法96%), 而非人工智能的算法成功率较低, 随机变量法62%, 回溯试探法84%. 应用人工智能方法特别是遗传算法和蚁群算法, 提升了自动化组卷效率, 满足了实际各种组卷的需要, 使其在远程教育和在线考试中有很好的应用前景.
关键词: 蚁群算法; 遗传算法; 数据库技术; 数学模型; 试题库
引用格式: 彭康华,黄裕锋,姚江梅.多种人工智能算法的数据库技术课程自动组卷比较.计算机系统应用,2018,27(3):210–216. https://www.wendangku.net/doc/b61580376.html,/1003-3254/6250.html
Comparison of Automatic Test Paper Generation for Database Technology Courses of Various Artificial Intelligence Algorithms
PENG Kang-Hua, HUANG Yu-Feng, YAO Jiang-Mei
(Information Engineering Institute, Guangdong Engineering Polytechnic, Guangzhou 510520, China)
Abstract: Online examination is widely used in distance education. Automated test paper is the key technology of online examination. The problem of generating test paper is the solution of multi-objective expected value, and it often has multiple solutions. For solving multi-objective function, the advantage of artificial intelligence algorithm is more and more obvious. Among them, the multi-objective optimization of genetic algorithm and ant colony optimization is more efficient, and can be more competent for the automatic test paper generation of the database technology curriculum. The application of artificial intelligence algorithm in test paper generation is discussed. The index system of test paper generation is constructed, and a mathematical model of multi-objective constraint is established, and the multi-objective expectation is optimized. The experiments results demonstrate that artificial intelligence algorithm has the highest success rate, with an average of more than 98% (including 100% of ant colony optimization, 96% of genetic algorithm), while those other than the artificial intelligence algorithm have low success rate, with the random variables 62%, backtracking method 84%. The application of artificial intelligence method, especially genetic algorithm and ant colony optimization, improves the efficiency of automated test paper generation. It meets the needs of various actual test paper generation, and makes online examination very well applied.
Key words: ant colony optimization; genetic algorithm; database technology; mathematical model; test question database
①基金项目: 广东省科技计划项目(2015A030303013, 2014A020217016)
收稿时间: 2017-06-13; 修改时间: 2017-07-12; 采用时间: 2017-07-14; csa在线出版时间: 2018-02-09
210软件技术?算法 Software Technique?Algorithm
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