自动组卷算法的研究
|
摘要
组卷系统是实现考试规范化、公平化、合理化的重要途径。以往的组卷系统的研究是为了使广大教师从繁重的手工编制试卷中解放出来，随着人工智能技术和计算机技术的深入应用，目前主要是对组卷算法和提高试卷的质量及系统运行效率的研究。本文在对国内外大量相关文献分析研究的基础上，设计了一种具有通用性的基于知识的组卷系统，提出了一种新的自动组卷方法，建立了组卷系统新的数学模型，并深入研究了其求解的方法。课题研究的过程中我们实现了一些主要算法，同时给出了一定的实例验证。
在本文所构建的组卷系统的基础上，我们分为获取用户要求、当前试卷模式的设计和试卷模式的实现。首选获取用户要求后，我们根据试题和试卷指标间的数学关系将其量化成描述目标试卷特征的一组指标分数分布列，即当前试卷模式；然后建立基于当前试卷模式的最接近用户的多目标优化模型，并对模型进行求解。在求解过程中，我们引入惩罚函数法和权重系数法，将有约束的多目标优化模型转化为无约束单目标优化模型，进而利用自适应遗传算法对其进行求解，解集即为目标试卷中具有相同试题指标取值的试题数量。最后在多目标优化数学模型的基础上，结合以课程知识结构图为依据的优先入选知识点算法，获得了一组或多组目标试题集，最终实现计算机的自动组卷。
本文首次提出了基于当前试卷的多目标优化的数学模型，并创造性的将惩罚函数、权重系数法和遗传算法结合起来对其求解。同时本文组卷算法和组卷算法所依托的组卷系统也是新颖的。
本文所采用的组卷算法以及在自动组卷数学模型的建立与求解上的深入研究将有利于组卷算法的发展。
Test paper generating system is one of the important approach to realize the standardization, justice and rationalization in the examination. Different from just freeing teachers from the trouble of manually generating test paper, now focus has been changed to the research of generating algorithm and how to improve the quality of the test item and the efficiency of test paper generating system, with even broad application of artificial intelligent technology and computer technology. Based on analysis of plenty of relative articles in overseas or domestic, this article designed a common generating examination paper system on expert knowledge, gave the new method to realize automatic generating examination paper, established a new mathematic to realize automatic generating examination paper, and research the method of getting result deeply. During the research, we realized some of the main algorithms, which would be shown in several examples.
On the basis of the system this article constructed, the process of how to generate a series of qualified test papers was subscribed in detail from three steps of acquisition of user demands, design of test model and realization of test paper. First, according to mathematic relationships of some index between test items and demand of test paper, user demands were quantified, after acquisition step, to a group of discrete indexes’ distributive columns, which are used to describe the characters of objective test paper, namely the model of present test paper. Secondly, based on present test paper model, a multi-objective optimization model, which seems much satisfying user demands, was established. In the process of solving the problem, penalty function was introduced to simplify the difficulty with which the constrained optimization problem was turn into an unconstrained one. Then we found the solution by the use of genetic algorithm. The set of solution is just the quantity of test items that have the index value in objective test paper. At last based on the optimization results of multi-objective mathematic model and the preferred selection algorithm
according to the prior of graphic of knowledge relationship, we got a group of or several group of objective test items. Until now we finally realized the automatic generating examination paper of computer.
This article designed the multi-objective optimization model based on current examination paper mode and got the result by combination of penalty function algorithm and genetic algorithm for the first time. At the same time, the design and method of examination paper generating algorithm are unique.
The model of generating examination paper system and mathematic model involved in this article will contribute to the generating examination paper system. All these work will help the development of generating examination paper system.
在本文所构建的组卷系统的基础上，我们分为获取用户要求、当前试卷模式的设计和试卷模式的实现。首选获取用户要求后，我们根据试题和试卷指标间的数学关系将其量化成描述目标试卷特征的一组指标分数分布列，即当前试卷模式；然后建立基于当前试卷模式的最接近用户的多目标优化模型，并对模型进行求解。在求解过程中，我们引入惩罚函数法和权重系数法，将有约束的多目标优化模型转化为无约束单目标优化模型，进而利用自适应遗传算法对其进行求解，解集即为目标试卷中具有相同试题指标取值的试题数量。最后在多目标优化数学模型的基础上，结合以课程知识结构图为依据的优先入选知识点算法，获得了一组或多组目标试题集，最终实现计算机的自动组卷。
本文首次提出了基于当前试卷的多目标优化的数学模型，并创造性的将惩罚函数、权重系数法和遗传算法结合起来对其求解。同时本文组卷算法和组卷算法所依托的组卷系统也是新颖的。
本文所采用的组卷算法以及在自动组卷数学模型的建立与求解上的深入研究将有利于组卷算法的发展。
Test paper generating system is one of the important approach to realize the standardization, justice and rationalization in the examination. Different from just freeing teachers from the trouble of manually generating test paper, now focus has been changed to the research of generating algorithm and how to improve the quality of the test item and the efficiency of test paper generating system, with even broad application of artificial intelligent technology and computer technology. Based on analysis of plenty of relative articles in overseas or domestic, this article designed a common generating examination paper system on expert knowledge, gave the new method to realize automatic generating examination paper, established a new mathematic to realize automatic generating examination paper, and research the method of getting result deeply. During the research, we realized some of the main algorithms, which would be shown in several examples.
On the basis of the system this article constructed, the process of how to generate a series of qualified test papers was subscribed in detail from three steps of acquisition of user demands, design of test model and realization of test paper. First, according to mathematic relationships of some index between test items and demand of test paper, user demands were quantified, after acquisition step, to a group of discrete indexes’ distributive columns, which are used to describe the characters of objective test paper, namely the model of present test paper. Secondly, based on present test paper model, a multi-objective optimization model, which seems much satisfying user demands, was established. In the process of solving the problem, penalty function was introduced to simplify the difficulty with which the constrained optimization problem was turn into an unconstrained one. Then we found the solution by the use of genetic algorithm. The set of solution is just the quantity of test items that have the index value in objective test paper. At last based on the optimization results of multi-objective mathematic model and the preferred selection algorithm
according to the prior of graphic of knowledge relationship, we got a group of or several group of objective test items. Until now we finally realized the automatic generating examination paper of computer.
This article designed the multi-objective optimization model based on current examination paper mode and got the result by combination of penalty function algorithm and genetic algorithm for the first time. At the same time, the design and method of examination paper generating algorithm are unique.
The model of generating examination paper system and mathematic model involved in this article will contribute to the generating examination paper system. All these work will help the development of generating examination paper system.
引文
项国雄.计算机辅助教学原理与课件设计.电子科技大学出版社,1997:17-35
李文兵.考试学概论.长春:东北大学出版社,1991:25-49
徐玖平.考试学.成都科技大学出版社,1989:12-45
张松.《微生物学》试题库计算机管理系统的研究.华南师范大学学报(社会科学版),
1999,5:77-88
郭文夷.关于试题库管理系统的研制.上海第二工业大学学报,1991,(1):57-62
杨莉.计算机辅助命题系统.甘肃工业大学学报,1997,(4):72-75
赵建平.通用组卷系统的研制与开发.长春光学精密机械学院学报, 2001,24(2):60-63
赵化桥, 尹香莲.高等化学试题库应用于教学质量评估初探.教育与现代化,1996,27 (2): 56-58
铁汉, 王建明.考试成绩与试卷质量分析系统.电脑与信息技术,1999,(2):31-33唐棠.计算机应用类课程考试改革初探.五邑大学学报(自然科学版),1997,11(2)65-70
孟志青.通用试题库管理系统的一种优化命题模型.计算机工程与设计,1998,19(3):55-58
张庆.题库设计方案.沈阳教育学院学报,1999,(4):110-113
李莉, 何桂华, 杨晓萍.试卷定量分析系统.长春邮电学院学报,1995,2(15):38-42
林复华.智能成卷系统的数学模型、算法及其应用.[华南理工大学工学硕士论文].1992:21-36
R.H.Austing. The GRE Advanced Test in Computer Science.IEEE Computer, Dec,1997: 129-133
E.B.Kottman, S.E.Blount.Artificial Intelligence and Automatic Programming in CAI.
AI, 1975, 6:215-234
朱明, 王俊普, 王晶杰.通用试题库开发系统,计算机工程,1996,22(3):451-460
朱旭.高等数学试题库系统的开发与研究.教学与教材研究,1995,(2):29-30
徐娟芬, 袁晓东.PASCAL题库系统的设计和实现.计算机应用,1998,49(6):16-19
蒋康明.物理试题库智能组卷系统PBICS的研究与实现.[华南理工大学硕士学位
论文].1990:34-39
熊伟清, 胡军.一种题库模型与组卷算法.兰州铁道学院学报,1996,89(6):85-88
陈碧人.程序设计语言计算机题库系统设计.计算机应用研
究,1991,46(5):17-18
池抚新, 沈丽, 孙桂兰.计算机随机抽题组卷算法与应用.抚顺石油学院学报,1999,
19(2):45-47
阮文惠.一个面向应用的随机抽题考试系统.甘肃教育学院学报(自然科学版),2000,
(7):22-26
常守金.计算机辅助教育简明教程.天津:天津科学技术出版社,1994:161-167
张国珠, 戴时超.工程制图试题库开发中的关键问题的探讨.微型电脑应用,2000,
16 (1):59-60
丽雯, 陈渝光, 刘巍.一种有效的试题库框架设计算法.计算机应用,2000, (1):60-61
王晓娜, 肖京, 董晓梅等.通用智能试题库系统的研究.东北大学学报(自然科学
版),1999,20(2):155-158
华如海, 王俊普, 郑全等.基于约束满足的智能组卷方法的研究与实现.计算机应
用研究,2000,11:20-23
谢平.基于框架模式试题库智能组卷系统.华东交通大学学报,1998,15(4):58-63
文忠林, 蔡清万, 李元香.试题库智能组卷的遗传算法.湖北民族学院学报(自然科
学版),2000,18(3):53-55
田翔, 陈国良.广东省自学考试试题库(成卷)专家系统的设计与实现.计算机应用研究,1999,(5):76-79
陈逢凯, 李贵荣.框架化通用试题试卷设计系统开发.信息技术,2001,8:6-7
傅冬绵.题库管理与试卷生成系统的开发与应用.漳州师范学院学报(自然科学版), 2001,14(3):42-44
全惠云, 范国闯, 赵霆雷.基于遗传算法的试题库智能组卷系统研究.武汉大学学报(自然科学版),1999,56(5):58-60
张晔.线性目标规划法—一种实用的多目标优化设计方法.安徽大学学报(自然科学版),1997,27(1):71-76
胡达.实用多目标最优化.上海:上海科学技术出版社,1999:54-68
J.L.Udy. Computation of Interference between Three-dimensional Objects and the Optimal Packing Problem. Advance in Engineering Software,1988,10(1):8-14
C.S.Li, P.Syu, V.Castel.li. Hierarchy Scan: A hierarchical Similarity Search Algorithm for Databases of Long Sequences,Proc.21st Int’l Conf.Data Eng.,1996:156-189
刘勇, 康立山.非数值并行方法(第2期),遗传算法.北京:科学出版社,1995:19-37
陈国良, 王煦法, 庄镇泉等.遗传算法及其应用.人民邮电出版社,1999:11-34
D.E.Goldberg.Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley,1989:23-56
张晓绩, 戴冠中, 徐乃平.一种新的有划算法—遗传算法.控制理论与应用, 1995,
12(3):265-273
J.H.Holland. Adaption in Natural and Artificial Systems. MIT Press.1992:6-24
A.Varsek, T.Urbance, Filipic B. Genetic Algorithm in Controller Design and Turning.IEEE Transactions on Systems,Man and Cybernetics. 1993,25(5):1330-1339
刘民, 吴澄, 蒋新松.用遗传算法解决并行多机调度问题.系统工程理论与实践.1998, 18(1):14-17
A.Gursel Suer, B.Eduardo.Minimizing the Number of Tardy Jobs in Identical Machine Scheduling. Computers and Industrial Engineering.1993,25(4):243-246
K.A.Johy. A daptive System Design:A genetic Approach.IEEE trans.Systems,Man and Cybernetics.1980,SMC-10(9):566-574
D.E.Goldberg.Controlling Dynamic Systems with Genetic Algorithms and Rule Learning.In Proceeding of the 9th International Joint Conference on Artificial Intelligence. 1987:588-592
T.Yamada, R.Nakano. A Gentic Algorithm Applicable to Large-Scale Job-Shop Problems.Proc of Parallel Problem Solving from Natrue II,1992:281-290
董聪, 郭晓华.广义遗传算法的逻辑结构及全局收敛性的证明.计算机科学,1998,25 (5):38-42
A V.Annier. Genetic Learning Procedures in Distributed Enviroments. In Proceedings of the Second International Conference on Genetic Algorithms,1987:162-169
M.Ester, H.P.Kriegel,X.Xu. Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Indentification, Proc,4th Int’l Symp.Large Spatial Databases(SSD’95),Portland,Maine,1995:67-82
R.Ng, J.Han. Efficient and Effective Clustering Method for Spatail Data Mining,Proc.Int’l Conf. Very Large Databases,Santiago,Chile,1994:144-155
J C.Potts, T.Giddens, B.Yadav Surya. The Development and Evaluation of an Imprved Genetic Algorithm Based on Migration and Artificial Selection [J]. IEEE Transactions on Systems, Man and Cybernetics.
1994,24(1):73-86
M.Srinivas, L M.Patnaik. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithm[J].IEEE Transactions on Systems,Man and Cybernetics.1994,24(4):656-657
Z.Michalewicz, M.Schoenauer .Evolutionary Algorithms for Constrained Parameter Optimization Problems.Evolutionary Computation Journal,1996,4(1):1-32
R.John,Koza. Genetic Programming II. The MIT Press.1994:25-39
Lee, W.Cheol, C.Yung. Construction of Fuzzy Systems Using Least-squares Method and Genetic Algorithm.Fuzzy Sets and Systems,2003,137(3):297-323
M.Lothar. Theory of Genetic Algorithms.Theoretical Computer Science,2001,259
(1-2):1-61
C.Smith Greg, S.Smith Shana. An Enhanced Genetic Algorithm for Automated Assembly planning. Robotics and Computer Integrated Manufacturing,2002,18(5-6):
355-364
Louis, J.Sushil, Li Gong. Case Injected Genetic Algorithms for Traveling Salesman Problems.Information Sciences,2000,122(2-4):201-225
T.Lynda, C.Chrisment,Boughanem Mohand.Multiple Query Evaluation Based on Enhanced Genetic Algorithm.Information Processing and Management,
2003, 39(2):215-231
A.Sena Giuseppe , D.Megherbi, I.Germinal.Implementation of a Parallel Genetic Algorithm on a Cluster of Workstations:Traveling Salesman Problem,a Case Study, 2001,17(4):477-488
H.Barbosa, C C.Lemonge Afonso. A New Adaptive Penalty Scheme for Genetic Algorithms.Information Sciences,2003,156(3-4):215-251
J.Andre, P.Siarry, T.Dognon. An Improvement of the Standard Genetic Algorithm Fighting Premature Convergence in Continuous Optimization.Advances in Engineer- ing Software,2001,32(1):49-60
攻读硕士学位期间承担的科研任务与主要成果
1 刘彬, 陈大平, 潘越.一类整数性目标规划的遗传算法.计算机工程与科学,2003,3
2 刘彬, 陈大平.一种新的关联规则发现算法及应用研究.计算机与信息技术, 2003,5
刘彬, 陈大平, 潘越等.一种新的智能组卷方法的研究.计算机与信息技术, 2002,8
4 刘彬, 金涛, 陈大平.遗传算法在试题组卷中的应用.燕山大学学报,2002,3
5 刘彬, 李勇, 陈大平.一种基于知识的组卷系统策略库设计.计算机与信息技术， 2002,7
6 刘彬, 陈大平.一种基于自适应遗传算法的组卷模型求解. 控制工程(已录用)
李文兵.考试学概论.长春:东北大学出版社,1991:25-49
徐玖平.考试学.成都科技大学出版社,1989:12-45
张松.《微生物学》试题库计算机管理系统的研究.华南师范大学学报(社会科学版),
1999,5:77-88
郭文夷.关于试题库管理系统的研制.上海第二工业大学学报,1991,(1):57-62
杨莉.计算机辅助命题系统.甘肃工业大学学报,1997,(4):72-75
赵建平.通用组卷系统的研制与开发.长春光学精密机械学院学报, 2001,24(2):60-63
赵化桥, 尹香莲.高等化学试题库应用于教学质量评估初探.教育与现代化,1996,27 (2): 56-58
铁汉, 王建明.考试成绩与试卷质量分析系统.电脑与信息技术,1999,(2):31-33唐棠.计算机应用类课程考试改革初探.五邑大学学报(自然科学版),1997,11(2)65-70
孟志青.通用试题库管理系统的一种优化命题模型.计算机工程与设计,1998,19(3):55-58
张庆.题库设计方案.沈阳教育学院学报,1999,(4):110-113
李莉, 何桂华, 杨晓萍.试卷定量分析系统.长春邮电学院学报,1995,2(15):38-42
林复华.智能成卷系统的数学模型、算法及其应用.[华南理工大学工学硕士论文].1992:21-36
R.H.Austing. The GRE Advanced Test in Computer Science.IEEE Computer, Dec,1997: 129-133
E.B.Kottman, S.E.Blount.Artificial Intelligence and Automatic Programming in CAI.
AI, 1975, 6:215-234
朱明, 王俊普, 王晶杰.通用试题库开发系统,计算机工程,1996,22(3):451-460
朱旭.高等数学试题库系统的开发与研究.教学与教材研究,1995,(2):29-30
徐娟芬, 袁晓东.PASCAL题库系统的设计和实现.计算机应用,1998,49(6):16-19
蒋康明.物理试题库智能组卷系统PBICS的研究与实现.[华南理工大学硕士学位
论文].1990:34-39
熊伟清, 胡军.一种题库模型与组卷算法.兰州铁道学院学报,1996,89(6):85-88
陈碧人.程序设计语言计算机题库系统设计.计算机应用研
究,1991,46(5):17-18
池抚新, 沈丽, 孙桂兰.计算机随机抽题组卷算法与应用.抚顺石油学院学报,1999,
19(2):45-47
阮文惠.一个面向应用的随机抽题考试系统.甘肃教育学院学报(自然科学版),2000,
(7):22-26
常守金.计算机辅助教育简明教程.天津:天津科学技术出版社,1994:161-167
张国珠, 戴时超.工程制图试题库开发中的关键问题的探讨.微型电脑应用,2000,
16 (1):59-60
丽雯, 陈渝光, 刘巍.一种有效的试题库框架设计算法.计算机应用,2000, (1):60-61
王晓娜, 肖京, 董晓梅等.通用智能试题库系统的研究.东北大学学报(自然科学
版),1999,20(2):155-158
华如海, 王俊普, 郑全等.基于约束满足的智能组卷方法的研究与实现.计算机应
用研究,2000,11:20-23
谢平.基于框架模式试题库智能组卷系统.华东交通大学学报,1998,15(4):58-63
文忠林, 蔡清万, 李元香.试题库智能组卷的遗传算法.湖北民族学院学报(自然科
学版),2000,18(3):53-55
田翔, 陈国良.广东省自学考试试题库(成卷)专家系统的设计与实现.计算机应用研究,1999,(5):76-79
陈逢凯, 李贵荣.框架化通用试题试卷设计系统开发.信息技术,2001,8:6-7
傅冬绵.题库管理与试卷生成系统的开发与应用.漳州师范学院学报(自然科学版), 2001,14(3):42-44
全惠云, 范国闯, 赵霆雷.基于遗传算法的试题库智能组卷系统研究.武汉大学学报(自然科学版),1999,56(5):58-60
张晔.线性目标规划法—一种实用的多目标优化设计方法.安徽大学学报(自然科学版),1997,27(1):71-76
胡达.实用多目标最优化.上海:上海科学技术出版社,1999:54-68
J.L.Udy. Computation of Interference between Three-dimensional Objects and the Optimal Packing Problem. Advance in Engineering Software,1988,10(1):8-14
C.S.Li, P.Syu, V.Castel.li. Hierarchy Scan: A hierarchical Similarity Search Algorithm for Databases of Long Sequences,Proc.21st Int’l Conf.Data Eng.,1996:156-189
刘勇, 康立山.非数值并行方法(第2期),遗传算法.北京:科学出版社,1995:19-37
陈国良, 王煦法, 庄镇泉等.遗传算法及其应用.人民邮电出版社,1999:11-34
D.E.Goldberg.Genetic Algorithm in Search, Optimization and Machine Learning. Addison-Wesley,1989:23-56
张晓绩, 戴冠中, 徐乃平.一种新的有划算法—遗传算法.控制理论与应用, 1995,
12(3):265-273
J.H.Holland. Adaption in Natural and Artificial Systems. MIT Press.1992:6-24
A.Varsek, T.Urbance, Filipic B. Genetic Algorithm in Controller Design and Turning.IEEE Transactions on Systems,Man and Cybernetics. 1993,25(5):1330-1339
刘民, 吴澄, 蒋新松.用遗传算法解决并行多机调度问题.系统工程理论与实践.1998, 18(1):14-17
A.Gursel Suer, B.Eduardo.Minimizing the Number of Tardy Jobs in Identical Machine Scheduling. Computers and Industrial Engineering.1993,25(4):243-246
K.A.Johy. A daptive System Design:A genetic Approach.IEEE trans.Systems,Man and Cybernetics.1980,SMC-10(9):566-574
D.E.Goldberg.Controlling Dynamic Systems with Genetic Algorithms and Rule Learning.In Proceeding of the 9th International Joint Conference on Artificial Intelligence. 1987:588-592
T.Yamada, R.Nakano. A Gentic Algorithm Applicable to Large-Scale Job-Shop Problems.Proc of Parallel Problem Solving from Natrue II,1992:281-290
董聪, 郭晓华.广义遗传算法的逻辑结构及全局收敛性的证明.计算机科学,1998,25 (5):38-42
A V.Annier. Genetic Learning Procedures in Distributed Enviroments. In Proceedings of the Second International Conference on Genetic Algorithms,1987:162-169
M.Ester, H.P.Kriegel,X.Xu. Knowledge Discovery in Large Spatial Databases: Focusing Techniques for Efficient Class Indentification, Proc,4th Int’l Symp.Large Spatial Databases(SSD’95),Portland,Maine,1995:67-82
R.Ng, J.Han. Efficient and Effective Clustering Method for Spatail Data Mining,Proc.Int’l Conf. Very Large Databases,Santiago,Chile,1994:144-155
J C.Potts, T.Giddens, B.Yadav Surya. The Development and Evaluation of an Imprved Genetic Algorithm Based on Migration and Artificial Selection [J]. IEEE Transactions on Systems, Man and Cybernetics.
1994,24(1):73-86
M.Srinivas, L M.Patnaik. Adaptive Probabilities of Crossover and Mutation in Genetic Algorithm[J].IEEE Transactions on Systems,Man and Cybernetics.1994,24(4):656-657
Z.Michalewicz, M.Schoenauer .Evolutionary Algorithms for Constrained Parameter Optimization Problems.Evolutionary Computation Journal,1996,4(1):1-32
R.John,Koza. Genetic Programming II. The MIT Press.1994:25-39
Lee, W.Cheol, C.Yung. Construction of Fuzzy Systems Using Least-squares Method and Genetic Algorithm.Fuzzy Sets and Systems,2003,137(3):297-323
M.Lothar. Theory of Genetic Algorithms.Theoretical Computer Science,2001,259
(1-2):1-61
C.Smith Greg, S.Smith Shana. An Enhanced Genetic Algorithm for Automated Assembly planning. Robotics and Computer Integrated Manufacturing,2002,18(5-6):
355-364
Louis, J.Sushil, Li Gong. Case Injected Genetic Algorithms for Traveling Salesman Problems.Information Sciences,2000,122(2-4):201-225
T.Lynda, C.Chrisment,Boughanem Mohand.Multiple Query Evaluation Based on Enhanced Genetic Algorithm.Information Processing and Management,
2003, 39(2):215-231
A.Sena Giuseppe , D.Megherbi, I.Germinal.Implementation of a Parallel Genetic Algorithm on a Cluster of Workstations:Traveling Salesman Problem,a Case Study, 2001,17(4):477-488
H.Barbosa, C C.Lemonge Afonso. A New Adaptive Penalty Scheme for Genetic Algorithms.Information Sciences,2003,156(3-4):215-251
J.Andre, P.Siarry, T.Dognon. An Improvement of the Standard Genetic Algorithm Fighting Premature Convergence in Continuous Optimization.Advances in Engineer- ing Software,2001,32(1):49-60
攻读硕士学位期间承担的科研任务与主要成果
1 刘彬, 陈大平, 潘越.一类整数性目标规划的遗传算法.计算机工程与科学,2003,3
2 刘彬, 陈大平.一种新的关联规则发现算法及应用研究.计算机与信息技术, 2003,5
刘彬, 陈大平, 潘越等.一种新的智能组卷方法的研究.计算机与信息技术, 2002,8
4 刘彬, 金涛, 陈大平.遗传算法在试题组卷中的应用.燕山大学学报,2002,3
5 刘彬, 李勇, 陈大平.一种基于知识的组卷系统策略库设计.计算机与信息技术， 2002,7
6 刘彬, 陈大平.一种基于自适应遗传算法的组卷模型求解. 控制工程(已录用)