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计算机系统应用英文版:2020,29(12):80-86
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面向中学走班制排课的优化遗传算法
(1.中国科学院大学 工程科学学院, 北京 100049;2.中国科学院 软件研究所, 北京 100190;3.中国科学院大学, 北京 100049;4.中国科学院 沈阳计算技术研究所, 沈阳 110168;5.山东大学 大数据技术与认知智能实验室, 济南 250000;6.北京市中关村中学, 北京 100086)
New Optimized Genetic Algorithm for Middle School Class Arrangement
(1.School of Engineering Science, University of Chinese Academy of Sciences, Beijing 100049, China;2.Institute of Software, Chinese Academy of Sciences, Beijing 100190, China;3.University of Chinese Academy of Sciences, Beijing 100049, China;4.Shenyang Institute of Computing Technology, Chinese Academy of Sciences, Shenyang 110168, China;5.Big Data Technology and Cognitive Intelligence Laboratory, Shandong University, Jinan 250000, China;6.Zhongguancun Middle School of Beijing, Beijing 100086, China)
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Received:April 20, 2020    Revised:May 13, 2020
中文摘要: 针对新课改走班制教学多约束条件下新的排课问题, 本文提出一种新的优化遗传算法策略, 并构建出了一套已在某中学试运行的走班制排课系统, 新系统集成了学生选课模块、学生成绩模块、学生评测模块. 对比传统遗传算法, 本文首次提出的冲突染色体优化策略, 在遗传算法中新增冲突染色体算子, 在实验中排课效率提升了19.2%. 在自适应变异率优化条件下, 再通过加入冲突染色体, 利用其可以剪掉算法迭代过程中产生的无用解的特性, 实现既保证了解的搜索空间又加速算法收敛的效果. 在本文的研究和实验中, 还就走班制教学下学生自主选科及分班模式对排课影响进行了验证, 实验显示按照“选课组合”策略对学生进行分班, 再与教师、教室、时间等教育资源组合排课时, 效率得到更多的提升.
Abstract:In view of the new scheduling problem under the condition of multi constraints in the new curriculum reform, this study proposes a new optimized genetic algorithm strategy, and constructs a set of trial run scheduling system in a middle school. The new system integrates the student selection module, student performance module, and student evaluation module. Compared with the traditional genetic algorithm, the conflict chromosome optimization strategy is proposed in this study for the first time; in the genetic algorithm, a new conflict chromosome operator is added, which improves the efficiency of course arrangement by 19.2%. Under the condition of adaptive mutation rate optimization, by adding the conflict chromosome, we can cut off the useless solution in the iterative process of the algorithm, which can not only ensure the search space of the solution, but also accelerate the convergence of the algorithm. In addition, the effect of students’ independent choice of subjects and division of classes on class arrangement is verified. The experiment shows that the efficiency of students’ division of classes according to the strategy of “combination of course selection” and the combination of class arrangement with teachers, classrooms, time, and other educational resources can be improved even more.
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基金项目:中国科学院信息化专项(XXH13504-05)
引用文本:
张永宏,王永吉,付立军,李旭,胡胜文.面向中学走班制排课的优化遗传算法.计算机系统应用,2020,29(12):80-86
ZHANG Yong-Hong,WANG Yong-Ji,FU Li-Jun,LI Xu,HU Sheng-Wen.New Optimized Genetic Algorithm for Middle School Class Arrangement.COMPUTER SYSTEMS APPLICATIONS,2020,29(12):80-86