| 基于遗传与蚁群混合算法的智能组卷研究 |
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| 引用本文： | 王纯纲,刘志杰,谢晓尧.基于遗传与蚁群混合算法的智能组卷研究[J].贵州师范大学学报(自然科学版),2014(1):100-104. |
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| 作者姓名： | 王纯纲 刘志杰 谢晓尧 |
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| 作者单位： | 贵州师范大学贵州省信息与计算科学重点实验室,贵州贵阳550001 |
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| 基金项目： | 贵州省科技厅工业攻关项目（黔科合GZ字[2012]3017）;贵阳市科技局工业振兴科技计划（筑科合同[2012101] 12号） |
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| 摘 要： | 针对遗传组卷算法局部求解能力不足、容易早熟和退化对系统中的反馈信息利用不够的问题,以及蚁群组卷算法搜索初期信息素匮乏的缺点,充分利用遗传算法较好的全局搜索能力和蚁群算法较高的求解精度的优势,提出了一种遗传算法与蚁群混合算法的智能组卷策略。实验结果表明,与单一组卷算法相比,提出的混合组卷方法收敛速度更快,能更有效地解决智能组卷问题,具有更好的实用性。
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| 关 键 词： | 遗传算法 蚁群算法 智能组卷 试题库 |
Hybrid algorithm based on genetic and ant colony research of intelligent test paper |
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| WANG Chungang,LIU Zhijie,XIE Xiaoyao.Hybrid algorithm based on genetic and ant colony research of intelligent test paper[J].Journal of Guizhou Normal University(Natural Sciences),2014(1):100-104. |
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| Authors: | WANG Chungang LIU Zhijie XIE Xiaoyao |
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| Institution: | ( Key Laboratory of Information and Computing Sciences of Guizhou Province, Guizhou Normal University, Guiyang, Guizhou 550001, China) |
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| Abstract: | Genetic algorithmic partial solution for lack of capacity, and the premature degradation of the system in the problem of insufficient use of feedback information, as well as the initial ant phero- mone Algorithmic search for scarce shortcomings, make full use of genetic algorithms better global search capability and ant colony algorithm for solving high accuracy advantages, we propose a hybrid genetic algorithm and ant colony algorithm for intelligent test strategies. Experimental results show that compared with the single Algorithmic proposed hybrid test paper method converges faster, more effec- tive problem solving intelligent test paper has better practicability. |
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| Keywords: | genetic algorithms ant colony algorithm intelligent test paper test bank |
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