进化计算与粗糙集理论研究及其在图像处理中的应用
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摘要
计算智能方法往往具有自学习、自组织、自适应的特征和简单、通用、鲁棒性强、适于并行处理的优点。在并行搜索、联想记忆、模式识别、知识自动获取等方面得到了广泛的应用。进化计算与粗糙集理论是计算智能的两个热点研究方向，是目前信息科学、自动化科学、计算机科学的交叉和前沿研究领域。思维进化算法是模拟人类思维进化过程的一种新型进化计算方法。本文分别以进化计算和粗糙集为研究目标，研究内容分为四大部分，一是进化计算的数列模型及其在收敛性分析中的应用；二是基于种群信息熵的思维进化算法自适应搜索策略研究；三是基于位编码可分辨矩阵的决策规则获取算法的研究；四是基于思维进化算法和粗糙集的图像处理方法的研究。
具体内容包括：
1．从进化机制出发，研究了进化算法的种群进化的特点并定义了种群适值函数，进而建立了进化的数列模型，并分析了在该模型下几种典型进化算法的收敛特性；
2．研究了思维进化算法的进化机理，引入信息论中信息熵的概念，提出了基于种群进化熵的思维进化算法并在进化计算的统一框架下，证明了该算法在数列意义下是收敛的，数值优化实验表明该算法具有良好的性能；
3．研究了粗糙集理论的可分辨矩阵，提出了基于位编码可分辨矩阵规则获取策略，并将其成功应用于水泥窑炉运行操作的决策规则获取；
4．提出了基于粗糙集理论和思维进化算法的图像分析新方法，将基于种群进化熵的思维进化算法应用于数字图像分割的最佳阈值寻优；应用粗糙集理论中决策表建立形状分类机制，并应用基于位编码可分辨矩阵的规则获取算法，提取决策规则；将上述方法综合应用于染色体畸变分析系统的设计中。
本文的创新性成果包括：
(1)建立了进化计算方法的数列模型。把复杂的随机过程映射成为种群适值序列，从该序列的性质来分析种群的进化过程，从而便于用数学方法分析种群的进化过程，为进化计算理论研究提供了一种新方法；
(2)应用数列模型分析了几种典型进化算法的收敛性。给出了基于种群适值链的进化算法的收敛条件，使用区间套等定理证明了进化计算方法的全收敛性；
(3)提出了基于种群进化熵的思维进化算法。将信息论中信息熵的思想引入思维进化算法的进化操作设计，改进了思维进化算法的趋同操作，算法可根据种群进化信息估计种群进化熵，实现搜索区域自适应调整，提高了搜索效率；
(4)提出了基于位编码可分辨矩阵的规则获取策略。首先分析了可分辨矩阵求取属性值约简的可能性及合理性，进而提出了基于位编码可分辨矩阵规则获取算法。该算法以位编码可分辨矩阵为基础，实现属性和属性值约简，并将其应用于水泥窑炉运行操作的决策规则约简；
(5)将基于种群进化熵的思维进化算法和粗糙集理论分别应用于图像处理的图像分割和形状分类中。设计了基于思维进化算法与粗糙集理论的染色体畸变分析系统。
Computing Intelligence (CI) methods not only have the property of self-study, self-organizing, self-adaptive, but also is excellent for simple, current, strong robust and available for parallel process. Hereby, CI has been widely applied in parallel search, associateon memory, pattern recognition, knowledge automatic acquiring etc. Evolutionary Computing (EC) and Rough Sets Theory (RST) are the two hot directions for CI research and vest in the cross-frontier research area of the information science, the automation science and the computer science. Mind Evolutionary Algorithm (MEA) is a new kind of EC algorithm simulating the evolutionary course of human mind. This paper is based on the study of EC and RST and composed of four parts. First, study on numeral sequence model of EC and its application in convergency analysis. Second, study on Mind Evolutionary Algorithm Based on Population Entropy (PEMEA). Third, study on the decision rules reduct strategy in RST based on bit coded discemibility matrix. Fourth, study on digital image processing method based on PEMEA and RST.
Specifically, including the following sections:
1. Propose a numeral sequence model for evolutionary computation. A mapping between the evolutionary process of population and a numeral sequence consisted with population fitness function value is built. Thus the sequence reflects the status of the population in evolution. Then, the convergency of several typical evolutionary algorithms analyzed with the model.
2. Study the evolutionary mechanism and then introduce the concept of entropy into the design of MEA. A new MEA based on population evolutionary entropy is presented and its convergency is proved under the uniform frame of EC. Then numeral optimal experiment is done to analyze the algorithmic performance.
3. Present a decision-rule extraction algorithm based on bit-coded discernibility matrix in RST. At last, it is successfully used to extract the decision rules for course operation of cement kiln.
4. Present new methods of image processing using PEMEA and RST. PEMEA is employed to optimize the segmentation threshold and then the image is segmented with the optimal threshold. A form classification system is built with decision table in RST and the decision-rule extraction algorithm based on bit-coded discernibility matrix is used to reduct the decision rules. At last, an analysis system for chromosome aberrance is designed based on the two methods.
The innovations of this paper are as follows:
(1) Propose a numeral sequence model for EC. The complex random process of population evolution is mapped to a numeral sequence. Thus some properties are figured out by analyzing the sequence. That provides a new method for the theory research on EC.
(2) Employ the numeral sequence model of EC to analyze the convergency of several typical evolutionary algorithms. The convergence condition with population fitness chain is presented. Some theorems in functions analysis such as interval sheath theorem are used in the proof of their convergency.
(3) Present a new mind evolution algorithm based on population evolution entropy (PEMEA). The thinking of information entropy in information theory is introduced in the design of evolutionary operation for MEA. With the improved 'similartax' operator, the algorithm estimates the population evolutionary entropy and then automatically adjusts the control parameters accordingly. The self-adaptive search strategy improves the algorithmic efficiency.
(4) Present an extraction strategy based on bit-coded discernibility matrix. Firstly, the rationality of decision rules reduction with discemibility matrix is analyzed. Then a new decision-rule extraction algorithm is presented. The algorithm is based on bit-coded discemibility matrix, performing the reduction of both attributes and attributes' value. At last, it is successfully used to extract the decision rules for course operation of cement kiln.
(5) Respectively apply PEMEA and RST to image segmentation and image analysis. Design an analysis system for chromosome aberrance based on PEMEA and RST.
具体内容包括：
1．从进化机制出发，研究了进化算法的种群进化的特点并定义了种群适值函数，进而建立了进化的数列模型，并分析了在该模型下几种典型进化算法的收敛特性；
2．研究了思维进化算法的进化机理，引入信息论中信息熵的概念，提出了基于种群进化熵的思维进化算法并在进化计算的统一框架下，证明了该算法在数列意义下是收敛的，数值优化实验表明该算法具有良好的性能；
3．研究了粗糙集理论的可分辨矩阵，提出了基于位编码可分辨矩阵规则获取策略，并将其成功应用于水泥窑炉运行操作的决策规则获取；
4．提出了基于粗糙集理论和思维进化算法的图像分析新方法，将基于种群进化熵的思维进化算法应用于数字图像分割的最佳阈值寻优；应用粗糙集理论中决策表建立形状分类机制，并应用基于位编码可分辨矩阵的规则获取算法，提取决策规则；将上述方法综合应用于染色体畸变分析系统的设计中。
本文的创新性成果包括：
(1)建立了进化计算方法的数列模型。把复杂的随机过程映射成为种群适值序列，从该序列的性质来分析种群的进化过程，从而便于用数学方法分析种群的进化过程，为进化计算理论研究提供了一种新方法；
(2)应用数列模型分析了几种典型进化算法的收敛性。给出了基于种群适值链的进化算法的收敛条件，使用区间套等定理证明了进化计算方法的全收敛性；
(3)提出了基于种群进化熵的思维进化算法。将信息论中信息熵的思想引入思维进化算法的进化操作设计，改进了思维进化算法的趋同操作，算法可根据种群进化信息估计种群进化熵，实现搜索区域自适应调整，提高了搜索效率；
(4)提出了基于位编码可分辨矩阵的规则获取策略。首先分析了可分辨矩阵求取属性值约简的可能性及合理性，进而提出了基于位编码可分辨矩阵规则获取算法。该算法以位编码可分辨矩阵为基础，实现属性和属性值约简，并将其应用于水泥窑炉运行操作的决策规则约简；
(5)将基于种群进化熵的思维进化算法和粗糙集理论分别应用于图像处理的图像分割和形状分类中。设计了基于思维进化算法与粗糙集理论的染色体畸变分析系统。
Computing Intelligence (CI) methods not only have the property of self-study, self-organizing, self-adaptive, but also is excellent for simple, current, strong robust and available for parallel process. Hereby, CI has been widely applied in parallel search, associateon memory, pattern recognition, knowledge automatic acquiring etc. Evolutionary Computing (EC) and Rough Sets Theory (RST) are the two hot directions for CI research and vest in the cross-frontier research area of the information science, the automation science and the computer science. Mind Evolutionary Algorithm (MEA) is a new kind of EC algorithm simulating the evolutionary course of human mind. This paper is based on the study of EC and RST and composed of four parts. First, study on numeral sequence model of EC and its application in convergency analysis. Second, study on Mind Evolutionary Algorithm Based on Population Entropy (PEMEA). Third, study on the decision rules reduct strategy in RST based on bit coded discemibility matrix. Fourth, study on digital image processing method based on PEMEA and RST.
Specifically, including the following sections:
1. Propose a numeral sequence model for evolutionary computation. A mapping between the evolutionary process of population and a numeral sequence consisted with population fitness function value is built. Thus the sequence reflects the status of the population in evolution. Then, the convergency of several typical evolutionary algorithms analyzed with the model.
2. Study the evolutionary mechanism and then introduce the concept of entropy into the design of MEA. A new MEA based on population evolutionary entropy is presented and its convergency is proved under the uniform frame of EC. Then numeral optimal experiment is done to analyze the algorithmic performance.
3. Present a decision-rule extraction algorithm based on bit-coded discernibility matrix in RST. At last, it is successfully used to extract the decision rules for course operation of cement kiln.
4. Present new methods of image processing using PEMEA and RST. PEMEA is employed to optimize the segmentation threshold and then the image is segmented with the optimal threshold. A form classification system is built with decision table in RST and the decision-rule extraction algorithm based on bit-coded discernibility matrix is used to reduct the decision rules. At last, an analysis system for chromosome aberrance is designed based on the two methods.
The innovations of this paper are as follows:
(1) Propose a numeral sequence model for EC. The complex random process of population evolution is mapped to a numeral sequence. Thus some properties are figured out by analyzing the sequence. That provides a new method for the theory research on EC.
(2) Employ the numeral sequence model of EC to analyze the convergency of several typical evolutionary algorithms. The convergence condition with population fitness chain is presented. Some theorems in functions analysis such as interval sheath theorem are used in the proof of their convergency.
(3) Present a new mind evolution algorithm based on population evolution entropy (PEMEA). The thinking of information entropy in information theory is introduced in the design of evolutionary operation for MEA. With the improved 'similartax' operator, the algorithm estimates the population evolutionary entropy and then automatically adjusts the control parameters accordingly. The self-adaptive search strategy improves the algorithmic efficiency.
(4) Present an extraction strategy based on bit-coded discernibility matrix. Firstly, the rationality of decision rules reduction with discemibility matrix is analyzed. Then a new decision-rule extraction algorithm is presented. The algorithm is based on bit-coded discemibility matrix, performing the reduction of both attributes and attributes' value. At last, it is successfully used to extract the decision rules for course operation of cement kiln.
(5) Respectively apply PEMEA and RST to image segmentation and image analysis. Design an analysis system for chromosome aberrance based on PEMEA and RST.
引文
[1] 史忠植．高级人工智能．北京：科学出版社，1998．
[2] 蔡白兴，徐光祜．人工智能及其应用，第三版．北京：清华大学出版社，2004．
[3] 王永庆．人工智能原理与方法．西安：西安交通大学出版社，1998．
[4] Feigenbaum, E.A. The art of artificial intelligence, Themes and case studies in knowledge engineering, IJCAI5: 1014-1029.
[5] 董聪，郭晓华．智能计算中的热点问题．计算机科学，1999，26(4)：5-9．
[6] 王正志，薄涛．进化计算．北京：国防科技大学出版社．2000．
[7] 潘正君，康立山，陈毓屏．演化计算．北京：清华大学出版社1998．
[8] Wang Chuanlong, Xie Keming. Convergence of a new evolutionary computation algorithm in continuous state space. International Journal of Computer Mathematics, 2002, 79(1): 27-37.
[9] Goldberg D.E. Computer-aided Gas Pipeline Operation Using Genetic Algorithms and Rule Learning. Doctoral dissertation, University of Michigan, 1983.
[10] Goldberg D.E. Genetic Algorithms and Walsh Functions: Part Ⅰ, a Gentle Introduction. Complex Systems, 1989, 3: 129-152.
[11] Goldberg D.E. Genetic Algorithms and Walsh Functions: Part Ⅱ, Deception and its Analysis. Complex Systems, 1989, 3: 153-171.
[12] Goldberg D.E., Milman K, Tidd C. Genetic Algorithms: A Bibliography. IlliGAL Technical Report 920008,1992.
[13] Goldberg D.E. A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-oriented Simulated Annealing. Complex Systems, 1990, 4(4): 445-460.
[14] Goldberg D.E., Bridges C.L. An Analysis of a Reordering Operator on a GA-hard problem. Biological Cybernetics, 1990, 62: 397-405.
[15] Harik G.R., Lobo F.G, Goldberg D.E. The Compact Genetic Algorithms. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 287-297.
[16] Goldberg D.E. Genetic Algorithms in Search. Optimization and Machine Leaning, Addison-Wesley, 1989.
[17] Kapur J.N., Kesavan H.K. Entropy Optimization Principles with Applications, Academic Press, 1992: 1-150.
[18] Kargupta H. Information Transmission in Genetic Algorithm and Shannon's Second Theorem, IlliGAL Report: http://gal4.ge.uiuc.edu/illigal.home.htm.
[19] Grefenstette J.J. Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics, 1986,16(1): 122-128.
[20] Michalewicz Z. Genetic Algorithms+Data Structures=Evolution Programs. Springer, 1996: 209-237.
[21] Michalewicz Z., D.Dasgupta, R.G.L.Riche, M.Schoenaurer. Evolutionary Algorithms for Constrained Engineering Problems. Computers and Industrial Engineering, 1996, 30(4): 851-870.
[22] Homaifar A., Qi X., Lai H. Constrained Optimization Via Genetic Algorithms. Simulation, 1994,62(4): 242-253.
[23] Coello C.A.C. Use of a Self-Adaptive Penalty Approach for Engineering Optimization Problems. Computers in Industry, 2000, 41:113-127.
[24] 潘正君，康立山，陈毓屏．演化计算．清华大学出版社，广西科学技术出版社，1998．
[25] 刘勇，康立山，陈毓屏．非数值并行算法(第二册：遗传算法)．北京，科学出版社，1997．
[26] 周红松．演化算法理论及其在有序问题中的应用，[硕士学位论文]．武汉，武汉大学，1995．
[27] 郭涛．模式演化算法及其在工作站群上的并行实现，[硕士学位论文]．武汉，武汉大学．1996．
[28] 何军．人工生态计算模型的研究，[博士学位论文]．武汉，武汉大学，1995．
[29] 潘正君．基于演化计算的并行问题求解，[博士学位论文]．武汉，武汉大学，1996．
[30] 陈国良，王煦法，庄镇泉，王东生．遗传算法及其应用．北京，人民邮电出版社，1996．
[31] 王小平，曹立明．遗传算法—理论、应用与软件实现．西安：西安交通大学山版社．2003．
[32] 曾建潮，查凯．用于求解数值优化问题的思维进化方法．见毛剑琴，曹希仁，第三届全球智能控制与自动化大会论文集，中国合肥，2000：126-128．
[33] Yan Gaowei, Xie Gang, Qiu Yuxia, etc. MEA Based Nonlinearity Correction Algorithm for the VCO of LFMCW Radar Level Gauge. The Tenth International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), August 31 st-September 3rd, 2005, University of Regina, Canada: 461-470.
[34] Sun Yah, Sun Yu, Sun Chengyi. Clustering and Reconstruction of Color images Using MEBML. In, Proc. of Inter. Conf. on Neural Networks & Brain, Beijing, China, 1998: 361-365.
[35] Sun Chengyi, Sun Yan Sun, Sun Yu. Model-Selection-Based Economic Prediction System Using MEBML. In, Proc. of IEEE Int. Conf. on Systems, Man, and Cybernetics, Tokyo, Japan, 1999: 628-633.
[36] Xie Gang, Gao Jinlan, Xie Keming. Fuzzy Modeling Based on Rough Sets and Mind Evolutionary Algorithm. In, Proc. of the Sixth International Conference on Electronic Measurement and Instruments, Taiyuan, China, 2003:109-112.
[37] 邱玉霞，谢刚，谢克明．基于思维进化算法的fuzzy控制系统没计与仿真．计算机科学，2005，32(8A)：112-114．
[38] Qiu Yuxia, Xie Gang, Xie Keming. Optimal Fuzzy Controller Design Based on Mind Evolutionary Algorithm. in, Proc. of 10th International Symposium On Integrated Circuits, Devices & Systems, Singapore, 2004:1124-1128.
[39] 郭刚，谢刚，谢克明．一种新的基于MEA的白适应模糊控制器．太原理工大学学报，2004，35(3)：247-250．
[40] 郝晓丽，谢克明．多级思维进化寻优的模糊PID控制器设计．太原理工大学学报，2004，35(2)：130-133．
[4l] 杜永贵，谢刚，谢克明．基于思维进化算法的模糊白适应控制系统．见，席裕庚，曹希仁，郭雷，第四届全球智能控制与自动化大会论文集，中国上海，2002：1887-1890．
[42] 邱玉霞，谢克明．一种优化模糊控制规则的新方法．太原理工大学学报，2004，35(3)：254-256．
[43] 韩晓霞，谢克明．基于思维进化算法的模糊自寻优控制．太原理工大学学报，2004，35(5)：523-525．
[44] 唐晓琪．基于思维进化算法的神经网络仿人智能控制策略在倒立摆系统中的研究，[硕士学位论文]．太原，太原理工大学，2003．
[45] Brady R M. Optimization strategies leaned from biological evolution. Nature, 317,804-806, 1985.
[46] Ftp://lumpi.informatik.uni-dortmund.de/pub/EA/paper/ea-app.ps.gz.
[47] Chun J. S, Jung H. K, HaHn S. Y. A study on comparison of optimization of performance between immune algorithm and other heuristic algorithms Magentics. 1998 34(5): 2972-2975.
[48] 孙承意，谢克明，程明琦，基于思维进化及其学习的框架及新进展，太原理工大学学报发，1999，30(5)：453-457．
[49] 王川龙，孙承意．基于思维进化的智能算法的收敛性研究．计算机研究与发展．2000，37(7)：838-842．
[50] Xie Keming, Mou Changhua, Xie Gang. A MEBML-based adaptive fuzzy logic controller. In, 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, 21st Century Technologies and Industrial Opportunities, October 22-28, 2000, Nagoya Congress Center, Nagoya, Japan: 1492-1496.
[51] 孙承意等，MEC应用于图像分析中的综述，计算机工程与应用，2004(31)：90-93．
[52] Pawlak Z. Rough sets. International journal of information and computer science, 1982, 11(5): 341～356.
[53] Pawlak Z. Rough sets: Theoretical Aspects of Reasoning about data. Boston: Kluwer Academic Publishers, 1991.
[54] Slowinski R. Intelligent Decision Support: Handbook of Application and Advances of the Rough Sets Theory, Kluwer Academic Publishers, 1992.
[55] 吴明芬．粗糙集理论的研究现状与前景．五邑大学学报(自然科学版)，2002，16(2)：16～21．
[56] Szladow A. Datalogic/R: Mining the knowledge in database. PC AI, 1993, 7(1), 40～41.
[57] Slowinski R. Rough set approach to decision analysis. AI Expert, March, 1995.
[58] Pawlak Z. Rough sets. Communications of ACM, November, 1995: 89～95.
[59] Bonikowski Z. Algebraic structures of rough sets, In: W. Ziarko, ed. Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag, 1994, 242～247.
[60] Bryniarski E. Formal description of rough sets. In: W. Ziarko, ed. Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag, 1994, 208～216.
[61] Lin T Y, Liu Q, Yao Y Y. Logic system for approximate reasoning: via rough sets and topology. Ras Z W, Zemankiva M eds. Methodologies for Intelligent Systems, Berlin: Springer-Verlag, 1994, 65～74.
[62] Yao Y Y, Lin T Y. Generalization of rough sets using modal Logics. Intelligent Automation and Soft Computing. 1996, 2(2): 103～120.
[63] Polkowski, L. T. On convergence of rough sets. Intelligent Decision support. Edited by Roman Slowinski. Kluwer, 1992, 305～311.
[64] 苗夺谦，王珏．粗糙集理论中知识粗糙性与信息熵关系的讨论．模式识别与人工智能，1998．11：34～40．
[65] 苗夺谦，王珏．粗糙集理论中概念与运算的信息表示．软件学报，1999，10：113～116．
[66] 张文修，吴伟志．粗糙集理论介绍和研究综述．模糊系统与数学，2000，14(4)：1～12．
[67] Lin T Y. A rough logic formalism for fuzzy controller: A hard and soft computing view. International Journal of Approximate Reasoning, 1996, 15: 395～414.
[68] Nakamura A. A rough logic based on incomplete information and its application. International Journal of Approximate Reasoning, 1996, 15: 367～378.
[69] Kuroki N. Rough ideals in semigroup. Information Science, 1997, 100: 139～163.
[70] Ziarko W. Variable precision rough sets model. Journal of Computer System Science, 1993, 46: 39～59.
[71] Katzberg, J D, Ziarko W. Variable precision rough sets with asymmetric bounds. Ziarko W P. Proceeding of RSKD' 93, Springer-Verlag, 1994, 167～177.
[72] Beynon Malcolm. Reducts within the variable precision rough sets model: A further investgation. European Journal of Operational Research, 2001, 134: 592～605.
[73] Beynon Malcolm J., Peel Michael J. Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega, 2001, 29(6): 561～576.
[74] 陈湘晖，朱善君，吉吟东，李永敏．扩展的粗糙集模型及其不确定性度量．清华大学学报(自然科学版)，2002，42(1)：35～38．
[75] Marzena Kryszkiewicz. Rough set approach to incomplete information systems. Information Science, 1998, 112: 39～49.
[76] Slowinski Roman, Vanderpooten Daniel. A Generalized Definition of Rough Approximations Based on Similarity. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(2): 331～336.
[77] Liang Jiye, Xu Zongben. Uncertainty measures of roughness of knowledge and rough sets in incomplete information systems. Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on, 2000, vol. 4: 2526～2529.
[78] Wang Guoyin. Extension of rough set under incomplete information systems. Fuzzy Systems, 2002, vol.2: 1098～1103.
[79] Greco Salvatore, Matarazzo Benedetto, Slowinski Roman. Rough sets methodology for sorting problems in presence of multiple attributes and criteria. European Journal of Operational Research, 2002, 138: 247～259.
[80] 夏雨佳，李少远，席裕庚．一种基于粗糙集的信息系统决策规则获取方法．控制与决策，2001，16(5)：577～580．
[81] Lenarcik A., Piasta Z. Discretization of Condition Attributes Space Intelligent Decision Support. Edited by Roman Slowinski Kluwer, 1992, 373～389.
[82] 陈湘晖，朱善君，吉吟东．与特征选取和离散化集成的决策规则挖掘方法．系统工程理论与实践，2001，21(11)：1～7，30．
[83] Guan J. W., Bell D. A. Rough computational methods for information systems. Artificial intelligence, 1998, 105: 77～103.
[84] Guan J. W., Bell D. A.., Guan Z. Matrix computation for information systems. Information Science, 2001, 131: 129～156.
[85] Shan N., Ziarko W. An Incremental Learning Algorithm for Constructing Decision Rules. Rough Sets, Fuzzy Sets and Knowledge Discovery. Edited by Ziarko, W. Springer-Verlag, 1994, 326～334.
[86] Maamari E, Rajski J. The dynamic reduction of fault simulation. Computer-Aided Design of Integrated Circuits and Systems, 1993, 12(1): 137～148.
[87] Longshaw T., Haines S. Dynamic rough sets. Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society, 1995, 292～295.
[88] Ziarko W. On Reduction of Knowledge Representation. Proc. Of the second International Sym. on Methodologies for Intelligent Systems, 1987, 99～113.
[89] Muraszkieqicz M., Rybinski H. Towards A parallel Rough Sets Computer. Rough Sets, Fuzzy Sets and Knowledge Discovery, Edited by Ziarko W, Springer-Verlag, 1994, 434～443.
[90] Lin T Y. Rough set theory in very large database. Proceedings of IMACS Multiconference, Lille, International Association for Mathematics and Computer in Simulation, 1996, vol. 2: 942～947.
[91] Nguyen S H, Skowron A., Synak P. Knowledge discovery in databases: Rough sets approach. Proc. of IFSA' 97, Prague, 1997, 204～209.
[92] Pawlak Z. Vagueness and uncertainty—a rough set perspective. Computation Intelligence, 1995, 11(2): 227～232.
[93] 陈奇南，梁洪峻．模糊集和粗糙集．计算机工程，2002，28(8)：138～140．
[94] Dubois D., Prade H. Rough fuzzy sets and fuzzy rough sets. International Journal of General system, 1990, 17: 191～209.
[95] Mohua Banerjee, Sankar K. P. Roughness of a fuzzy set. Infonnatics and Computer Science, 1996, 93: 235～246.
[96] Hashemi Ray R., Charlese. Eppeson, The integration of rough sets and neural network paradigms in predictive systems. Intelligent Engineering systems through artificial neural networks, 1999, 9: 499～504.
[97] Khoo Li-Pheng, Zhai Lian-Yin. A prototype genetic algorithm-enhanced rough set-based rule induction system. Computers in Industry, 2001, 46(1): 95～106.
[98] Zhai Lian-Yin, Khoo Li-Pheng, Fok Sai-Cheong. Feature extraction using rough set theory and genetic algorithms—an application for the simplification of product quality evaluation. Computers and Industrial Engineering, 2002, 43(4): 661～676.
[99] Chiu T., Lin T. Y. Tuning rough controllers by genetic algorithms. Soft Computing in Intelligent Systems and Information Processing, 1996, 326～331.
[100] 熊范纶，杭小树．基于粗糙集理论的示例学习研究．浙江大学学报(理学版)，2002，29(3)：346～354．
[101] 周东华，王文辉．基于遗传算法的一种粗糙集知识约简算法．系统仿真学报，2001，13(suppl)：91～93，96．
[102] 李玉榕．信息融合与智能处理的研究，博士学位论文．浙江大学，杭州，2001．
[103] 王熙法，王上飞，陈恩红，胡静．交互式遗传算法中收敛性及用户评估质量的提高．中国科学技术大学学报．，2002，32(2)：210～216．
[104] Lingras P., Davies C. Rough Genetic Algorithm. Zhong N, Skowron A eds. Proc 7th Intl Wksp on RSFD, Springer, 1999, 38～46.
[105] 刘健勤，刘其兴．一种矿井涌水量预测的新的进化计算算法．煤炭学报，1998，23(1)：107～200．
[106] Rival I. Ordered sets. Berlin: Reidel, 1982.
[107] 邹先霞，杜威等．一种基于粗集理论的概念格构造方法．华中师范大学学报(自科版)，2001，35(2)：146～149．
[108] Kent R E. Rough concept analysis. In: Ziarko W P ed. Rough Sets, and Fuzzy Sets and Knowledge Discovery (RSKD' 93), London: Springer-Verlag, 1994, 248～255.
[109] 胡可云，陆玉昌，石纯一．概念格及其应用进展．清华大学学报(自然科学版)，2000，40(9)：77～81．
[110] Wong S., Lingras P. The compatibility view of Shafer-Dempster theory using the concept of rough set. Methodologies for Intelligence Systems, 1989, 4: 33～42.
[111] Skowron A., et al. From rough set theory to evidence theory. In: Advances in the Dempster-Shafer theory of evidence, John Wiley & Sons, Inc., New York, 1994, 193～236.
[112] Yahia M. E., Mahmod R., Sulaiman N., Ahmad F. Rough neural expert systems. Expert Systems with Applications, 2000, 18(2): 87～99.
[113] Pawlak Zdzis, Iz. xl aw. Rough sets, decision algorithms and Bayes' theorem, European Journal of Operational Research, 2002, 136(1): 181～189.
[114] Pawlak Zdzis, lz. shtsls aw. Rough sets and intelligent data analysis. Information Sciences, 2002, 147(1-4): 1～12.
[115] Grzymala-Busse J. W.. LERS—A sustem for leafing from examples based on rough sets. In "Intelligent Decision Support: Handbook of Application and Advances of the Rough Sets Theory" edited by R. Slowinski, 1992.
[116] Mrozek A., et al. Methodology of Rough Controller Synthesis. IEEE International Conference on Fuzzy Systems, NJ, USA, 1996, 2: 1135～1139.
[117] Adam MROZEK. Use of Rough Sets and Decision Tables for Implementing Rule-Based Control of Industrial Processes. Computer and Infonnati0n Science, 1986, 34(5-6): 357～370.
[118] 谢克明，杨静．粗糙集理论及其在智能控制领域的应用前景．太原理工大学学报，1999，30(4)：338～342．
[119] 马志锋，刑汉承，郑晓妹．决策表中的不确定性研究．控制与决策，2000，15(6)：703～707．
[120] Nejman D. A rough set based method of handwritten numerals classification. Institute of Computer Science Reports, 18/94, Warsaw University of Technology, 1994.
[121] Swiniarski Roman W., Hargis Larry. Rough sets as a front end of neural-networks texture classifiers. Neurocomputing, 2001, 36(1-4): 85～102.
[122] Wojcik Z. M. Application of rough sets for edge enhancing image filters. Image Processing(IEEE International Conference), 1994, vol.2: 525～529.
[123] Mohabey A., Ray A. K. Rough set theory based segmentation of color images. Fuzzy Information Processing Society, NAFIPS, 2000: 338～342.
[124] 杨锦堂，刘美林，王慧敏，徐立中．粗糙集理论在图像增强中的应用．数据采集与处理，1999，14(3)：307～310．
[125] 杨锦堂，王慧斌，徐立中．基于粗糙集理论的图像增强方法．仪器仪表学，2000，21(5)：514～515，524．
[126] 徐立中，王慧敏．一种图象预处理的新算法．中国图象图形学报，1999，4(11)：911～915．
[127] Ahn B. S., Cho S. S., Kim C. Y.. The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 2000, 18: 65～74.
[128] Jelonek J., et al. Rough set reduction of attributes and their domains for neural networks. Computational Intelligence, 1995, 11(2): 339～347.
[129] Chen-Jimenez I. E., Kornecki A. et al. Software safety analysis using rough sets. Southeastcon '98, Proceedings of IEEE, 1998: 15～19.
[130] Lewis T., Perkowski M., Jozwiak L. Learning in hardware: architecture and implementation of an FPGA-based rough set machine. EUROMICRO Conference, 1999, vol. 1: 326～334.
[131] 管天云．多传感器信息融合研究，博士学位论文．浙江大学，杭州，1998．
[132] 张启忠，罗志增．粗集理论在信息融合中的应用．机电工程，2001，18(5)：29～31．
[133] Fernandez-Baizan M.C., Ruiz E.M., Sanchez J. M. Using RSDM to mine Socio-Economic Indicators. Proc. of 1997 IEEE International Conference on Computational Cybernetics and Simulation, 1997, 2: 1385-1389.
[134] Dimitras A. I., Slowinski R., Susmaga R., Zopounidis C. Business failure prediction using rough sets. European Journal of Operational Research, 1999, 114(2): 263-280.
[135] Tay Francis E.H., Shen Lixiang. Economic and financial prediction using rough sets model. European Journal of Operational Research, 2002,141 (3): 641～659.
[136] Komorowski Jan, 0hrn Aleksander. Modelling prognostic power of cardiac tests using rough sets. Artificial Intelligence in Medicine, 1999, 15(2): 167～191.
[137] Gorzalczany Marian B., Piasta Zdzislaw. Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support. Information Sciences, 1999, 120(1-4): 45-68.
[138] Teghem J., et al. Use of "rough sets" method to draw premonitory factors for earthquakes by emphasizing gas geochemistry: the case of a low seismic activity content in Belgium. Intelligent decision support: handbook of applications and advances of the rough sets theory, 1992, 165-179.
[139] R. Deja. Conflict model with negotiations. Institute of Computer Science Reports, 51/95, Warsaw University of Technology, 1995.
[140] Nurmi Hannu, Kacprzyk Janusz, Fedrizzi Mario. Probabilistic, fuzzy and rough concepts in social choice. European Journal of Operational Research, 1996, 95(2): 264-277.
[1] Holland J H. Adaptation in natural and artificial systems [M]. Ann Arbor: University of Michigen Press, 1975.
[2] Fogel L J, Owens A J, Walsh M J. Artificial intelligence through simulated evolution [M]. New York: John Wiley, 1966.
[3] Forgel D B. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks, 1994, 5(1): 85-95.
[4] 徐宗本，李国．解全局优化问题的仿生类算法(Ⅰ)——模拟进化算法．运筹学杂志，1995，14(2)：1-13．
[5] Michelewicz Z. Genetic Algorithms+Data Structure=Evolutionary Programs, 3rd ed. New York: Springer-Verlag, 1996.
[6] Dawid H A. A Markov chain analysis of genetic algorithms with a state dependent fitness function. Complex Systems, 1994, 8: 407-417.
[7] Mahfoud S W. Finite Markov chain models of an alternative selection strategy for the genetic algorithm. Complex Systems, 1993, 7: 155-170.
[8] Rudolph G. Convergence analynsis of canonical genetic algorithms. IEEE Tans Neural Networks, 1994, 5(1): 96-101.
[9] Suzuki J. A further result on the Markov chain model of genetic algorithms and its application to a simulated annealing-like strategy. IEEE Trans System, Man&Cybernetics, 1998, 28(1): 95-102.
[10] Nix A. E, Vose D M. Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence, 1993, 5: 79-88.
[11] Vose M D, Liepins G E. Punctuated equilibrium in genetic search. Complex Systems, 1991, 5: 31-44.
[12] Cerf R. The dynamoics of mutateon-selection algorithms with large population sizes. Annals of Institute Henri Poicare, 1996, 32(4): 455-508.
[13] Cerf R. Asymptotic convergence of genetic algorithms. Advances in Application Probability, 1998, 30(2): 5221-550.
[14] Davis T E; Principe J C. A simulated annealing like convergence theory for the simple genetic algorithm. In: Proc the 4th Int. Conf. On Genetic Algorithms, San Mateo, CA, 1991: 174-181.
[15] Francois O. Convergence in simulated evolutionary algorithms. Complex Systems, 1996, 10: 311-319.
[16] Peck C C, Dhawan A P. Genetic algorithms as global random search methods: An alterative perspective. Evolutionary computation, 1995, 3(1): 39-80.
[17] 潘正君，康立山等．演化计算．清华大学出版社，桂林：广西科学技术出版社，1998．
[18] Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, MA, 1989.
[19] J. H. Holland. Adaptation in Natural and Artificial Systems. Ann Arbor: Univ. Of Michigan Press, 1975.
[20] J. R. Koza. Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
[21] L. J. Fogel. Artificial Intelligence Throurgh Simulated Evolution. New York: John Wiley, 1996.
[22] Rechenberg. Cybernetic Solution Path of an Experimental Problem. Roy Aircr, Establ, Libr. Trans. 1222, Hants, U. K. Farnboroungh, 1965.
[23] 张涛．热力系统在线进化优化体系研究．博士学位论文．北京：清华大学2000．
[24] 王鑫鑫，徐向东．进化优化算法通用框架模型研究．电站系统工程，2002，1886：1～4．
[25] 王鑫鑫，徐向东．分层协同进化模型在热力系统在线优化中的应用研究．热能动力工程2002，174：339～341．
[26] 云庆夏．进化算法．北京：冶金工业出版社2000．
[27] 王小平，曹立明．遗传算法理论应用与软件实现．西安：西安交通大学出版社2002
[28] 云庆夏，黄光球，王战权．遗传算法和遗传规划一种搜索寻优技术．北京：冶金工业出版社．1997．
[29] 王正志，薄涛．进化计算．长沙：国防科技大学出版社，2000．
[30] Goldberg E. Genetic Algorithms in Search, Optimization and Machine Leaming. Reading, MA: Addison Wesley, 1989.
[31] Koza J. R. Genetic Programming: A paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems. Report No. STAN-CS-90-1314, Stanford University, 1990．
[32] Yao X, Liu Y. Fast evolutionary strategies[A]. P. J. Angeline, ed. Proceedings of the Sixth Annual Conference on Evolutionary Programming[C]. Berlin: Springer, 1997, 151161.
[33] Yang Y., Yang P. C., Huang C. L. Evolutionary Programming Based Economic Dispatch for Units with Non-smooth Fuel Cost Functions. IEEE PES Winter Power Meeting, USA, 1995.
[34] 张文修，梁怡．遗传算法的数学基础．西安：西安交通大学出版社，2000．
[35] 刘健勤．人工生命理论及其应用．北京冶金工业出版社．
[36] 赵达纲，朱迎善．应用随机过程．北京机械工业出版社，1990．
[37] 龚怀云，寿纪麟，王绵森，应用泛函分析，西安交通大学出版社，1995．
[38] Michalewicz, Z., Genetic Algorithms+Data Structure=Evolutionary Programs, Springer-Verlag, Berlin, Heidelberg, 2nd Edition, 1994.
[39] Rudolph, G. Convergence Analysis of Canonical Genetic Algorithms, IEEE Trans. On N. N. Vol. 5, No. 1, 1994.
[40] 刘健勤，魏敏洁．进化计算的可计算性．计算技术与自动化，Vol．17，No．3，1998，1-2．
[1] 蔡自兴，徐光祐．人工智能及其应用，第三版．北京：清华大学出版社，2004．
[2] Holland J H. Adaptation in nature and artificial system. Ann Arbor: University of Michigan Press, 1975: 35.
[3] Bethke A D. Genetic algorithms as function optimizers. Dissertation Abstracts International, 1980, 4(19): 48.
[4] Goldberg D E. Simple Genetic Algorithms and the Minimal, Deceptive Problem. In: L. Davis, Genetic Algorithms and Simulated Annealing, San Mateo, CA, Morgan Kaufman publisher, 1987: 74-88.
[5] Eshelman L J. The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. In: Gregory J E Rawlins. Foundations of Genetic Algorithms, San Mateo, CA, Morgan Kaufman publisher, 1991: 265-283.
[6] Louis S J, Rawlins G J E. Syntactic Analysis of Convergence in Genetic Algorithms. In: L Darrell Whitley, Foundations of Genetic Algorithms2, San Mateo, CA, Morgan Kaufmann publisher, 1993: 141-151.
[7] Whitley D, Hanson T. Optimizing neural networks using faster, more accurate genetic search. In: J. David Schaffer, Proceeding of the International Conference on Genetic Algorithms, George Mason University, Fairfax, Virginia, USA, June 1989: 391-396.
[8] Whitley D, Starkeather T, Bogart C. Genetic algorithms and neural networks: optimizing connection and connectivity. Parallel Computing, 1990, 14: 247-361.
[9] Yoon B, Holmes D J. Efficient genetic algorithms for training layered feed forward neural networks. Information Sciences, 1994,76(12): 67-85.
[10] Bhanderi D, Pal H R, Pal S K. Directed mutation in genetic algorithms. Information Sciences, 1994, 79(3): 251-270.
[11] Tsutsui S, Ghosh A, Corne D, et al. A Real Coded Genetic Algorithm with an Explorer and an Exploiter Populations. In: Proc. of the 7th International Conference on Genetic Algorithms, San Francisco, 1997: 238-245.
[12] Sun Zennqi, Wan Qian. A Modified Genetic Algorithm: Meta-Level Control of Migration in a Distributed GA. In: Proc. of IEEE International Conference on Evolutionary Computation, Piscataway, 1995
[13] Sun Chengyi, Sun Yan, Wei Lijun. Mind-Evolution-Based Machine Learning: Frame work and the Implementation of Optimization. In: Proc. of IEEE International Conference on Intelligent Engineering Systems, Vienna, Austria, 1998, 355-359.
[14] 孙承意，谢克明，程明琦．基于思维进化机器学习的框架及新进展．太原理工大学学报，1999，30(5)：453-457．
[15] Wang Chuanlong, Xie Keming. Convergence of a new evolutionary computation algorithm in continuous state space. International Journal of Computer Mathematics, 2002, 79(1): 27-37.
[16] 王川龙，孙承意．基于思维进化的MEBML算法的收敛性研究．计算机研究与发展，2000，37(7)：838-842．
[17] 孙承意，孙岩，谢克明．思维进化——高效率的进化计算方法．第三届全球智能控制与自动化大会论文集，中国合肥，2000：112-117．
[18] Wei Lijun, Sun Yan, Sun Chengyi. A more efficient similartaxis strategy of MEBML. In: Proc. of the IASTED Conf. on Modeling and Simulation, Pittsburgh, Pennsylvania, USA, Acpa Press, 1999: 5-8.
[19] 曾建潮．基于思维进化机器学习的自适应趋同策略．计算机工程与科学，2000，22(4)：95-107．
[20] 曾建潮，查凯．基于思维进化机器学习的异化策略研究．第三届全球智能控制与自动化大会论文集，中国合肥，2000：129-131．
[21] Sun Chengyi, Sun Yan. The Performance of a Modified MEBML System in Noisy Environment. In: Proc. of IEEE Intl. Conf. on Systems, Man and Cybernetics, Tokyo, Japan, 1999: 168-171.
[22] 曾建潮，查凯．用于求解数值优化问题的思维进化方法．第三届全球智能控制与自动化大会论文集，中国合肥，2000：126-128．
[23] Sun Yan, Sun Yu, Sun Chengyi. Clustering and Reconstruction of Color images Using MEBML. In: Proc. of Intl. Conf. on Neural Networks & Brain, Beijing, China, 1998: 361-365.
[24] Sun Chengyi, Sun Yan Sun, Sun Yu. Model-Selection-Based Economic Prediction System using MEBML. In: Proc. of IEEE Intl. Conf. on Systems, Man, and Cybernetics, Tokyo, Japan, 1999.
[25] Xie Gang, Gao Jinlan, Xie Keming. Fuzzy Modeling Based on Rough Sets and Mind Evolutionary Algorithm. In: Proc. of the Sixth International Conference on Electronic Measurement and Instruments, Taiyuan, China, 2003:109-112.
[26] 邱玉霞，谢刚，谢克明．基于思维进化算法的fuzzy控制系统设计与仿真．计算机科学，2005，32(8A)：112-114．
[27] Qiu Yuxia, Xie Gang, Xie Keming. Optimal Fuzzy Controller Design Based on Mind Evolutionary Algorithm. In: Proc. of 10th International Symposium On Integrated Circuits, Devices & Systems, Singapore, 2004: 1124-1128.
[28] 郭刚，谢刚，谢克明．一种新的基于MEA的自适应模糊控制器．太原理工大学学报，2004，35(3)：247-250．
[29] 郝晓丽，谢克明．多级思维进化寻优的模糊PID控制器设计．太原理工大学学报，2004，35(2)：130-133．
[30] 杜永贵，谢刚，谢克明．基于思维进化算法的模糊自适应控制系统．第四届全球智能控制与自动化大会论文集，中国上海，2002：1887-1890．
[31] 邱玉霞，谢克明．一种优化模糊控制规则的新方法．太原理工大学学报，2004，35(3)：254-256．
[32] 韩晓霞，谢克明．基于思维进化算法的模糊自寻优控制．太原理工大学学报，2004，35(5)：523-525．
[33] 唐晓琪．基于思维进化算法的神经网络仿人智能控制策略在倒立摆系统中的研究．[硕士学位论文]，太原，太原理工大学，2003．
[34] Xie Keming, Mou Changhua, Xie Gang. A MEBML-based adaptive fuzzy logic controller. In: 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, 21st Century Technologies and Industrial Opportunities, October 22-28, 2000, Nagoya, Japan: 1492-1496.
[35] Xie Keming, Mou Changhua, Gang Xie, et al. A new MEBML-based algorithm for adjusting parameters on-line. In: Proc. of 5th International Conference on Signal Processing, Beijing, 2000: 1714-1717.
[36] Xie Keming, Mou Changhua, Xie Gang. The multi-parameter combination mind-evolutionary-based machine learning and its application. Proceedings. of 2000 IEEE International Conference on Systems, Man, and Cybernetics, Music City, Tennessee, USA, 2000: 183-187.
[37] 谢克明，杜永贵，孙承意．基于思维进化机器学习算法在水泥生料配比中的应用．第三届全球智能控制与自动化大会论文集，中国 合肥，2000：132-134
[38] Shannon CE. The mathematical Theory of Communication [M]. Urbana: University of Illinois Press, 1992.
[39] Lee Jooyoung. New Monte Carlo algorithm: entropic sampling[J]. Physical Review Let, 1993, 71(1): 211214.
[40] Lee Chang Yong, Han Seung Kee. Evolutionary optimization algorithm by entropic sampling[J]. Physical Review E, 1998, 57(3): 36113617.
[41] 需庆生，信息论[M]．西安：西安交通大学出版社，1989．
[42] 龚怀云，寿纪麟，王绵森，应用泛函分析，西安交通大学出版社，1995，10-16．
[43] Michalewicz, Z., Genetic Algorithms+Data Structure=Evolutionary Programs, Springer-Verlag, Berlin, Heidelberg, 2nd Edition, 1994.
[44] Rudolph, G., Convergence Analysis of Canonical Genetic Algorithms, IEEE Trans. On N.N., Vol.5, No. 1, 1994.
[45] Michalewicz, Z. Genetic Algorithms+Data Structures=Evolution Programs. Berlin, Heidelberg, New York: Springer-Verlag, 1992.
[46] http://agat.ely.pg.gda.pl/～mkwies/dyd/sro/geadocu/fcnfun1b.html.
[47] 周明，孙树栋．遗传算法原理及应用，北京：国防工业出版社，2002．
[48] 潘正君，康立山，陈毓屏．演化计算，北京：清华大学出版社，1998．
[1] Pawlak Z. Rough sets: Theoretical Aspects of Reasoning about data. Boston: Kluwer Academic Publishers, 1991
[2] Duntsch I., Gediga G.. Uncertainty measures of rough set prediction. Artificial Intelligence, 1998, 106: 109-137.
[3] Pawlak Z. Rough set theory and its applications to data analysis. Cybernetics and Systems, 1998, 29: 661～688.
[4] Hu X. H., Cercone N. Learning In Relational Databases: A Rough Set Approach. International Journal of Computational Intelligence, 1995, 11(2): 323～338.
[5] 王国胤．Rough集理论与知识获取．西安交通大学出版社，西安，2001．5．
[6] Pawlak Z., Rough Sets, Theoretical Aspects of Reasoning about Data, Nowowiejska 15/19, Warsaw, Poland, 1990.
[7] Skowron A., Rauszer C. The discernibility matrices and functions[M]. In intelligent decision support. Handbook of applicastion and advancees of the rough set theory. Ed. R. Slowinski, 331-362. Boston: Kluwer Academic Publishers, 1991.
[1] 孙即祥．图像处理．北京：科学出版社．2004．
[2] 蔡自兴，徐光祐．人工智能及其应用，第三版．北京：清华大学出版社，2004．
[3] Pawlak Z. Rough sets. International journal of information and computer science, 1982, 11(5): 341～356.
[4] Pawlak Z. Rough sets: Theoretical Aspects of Reasoning about data. Boston: Kluwer Academic Publishers, 1991.
[5] 王正志，薄涛．进化计算．北京：国防科技大学出版社．2000．
[6] 潘正君，康立山，陈毓屏．演化计算．北京：清华大学出版社，1998．
[7] 薛景浩，章毓晋，林行刚．SAR图像基于Rayleigh分布假设的最小误差阈值化分割．电子科学学刊，1999，21(2)：219～2252．
[8] 陈哲，冯天谨．基于小波分形特征提取的图像分割方法．中国图象图形学报，1999，4A(12)：1072～1077．
[9] Tian B, Shaikh M A, Azimi Sadjadi M R, etal. A Study of Cloud Classification with Neural Networks Using Spectral and Textural Features. IEEE Trans. Neural Networks, 1999,10(1):138～151.
[10] Zhang Y, Smith S M, and Brady J M. Segmentation of brain MR images using Markov Random Field. Proceedings of Medical Image Understanding and Analysis' 99, available on http://www.ipg.umds.ac.uk/miua/.
[11] Mari Pierre Dubuisson Jolly, Sridhar Lakshmanan, and Anil K. Jain. Vehicle Segmentation and Classification Using Deformable Templates. IEEE Trans. Pattern Analysis and Maching Intelligence, 1996,18(3):293～308.
[12] Rob Koenen. MPEG4 Overview. Available on http://drogo.cselt.it/mpeg/standards/mpeg4/mpeg4.html.
[13] Swanson MD, Tewfik A H. Fast Progressively Refined Image Retrieval. Journal of Electronic Imaging, 1998, 7(3):443～452.
[14] 王爱民，沈兰荪．图像分割研究综述．测控技术，2000，19(5)：1-6．
[15] 姚敏等．数字图像处理．北京：机械工业出版社，2006．1．
[16] 章毓晋，图像工程(中册)图像分析(第2版)，北京：清华大学出版社，2003．
[17] Kenneth R．Castleman著，朱志刚等译．数字图像处理(Digital Image Processing)．北京：电子工业出版社， 2002．
[18] 边肇祺，张学工等．模式识别(第二版)．北京：清华大学出版社，2000．
[19] Costa L F, Cesar R M. Shape Analysis and Classification: Theory and Practice. CRC Press, 2001.
[20] ASM International. Practical Guide to Image Analysis. ASM International, 2000.
[21] 王国胤．Rough集理论与知识获取．西安：西安交通大学出版社，2001．5．
[22] 潘自强，辐射防护的现状与未示来，北京：原子能出版社，1997，218-230．
[2] 蔡白兴，徐光祜．人工智能及其应用，第三版．北京：清华大学出版社，2004．
[3] 王永庆．人工智能原理与方法．西安：西安交通大学出版社，1998．
[4] Feigenbaum, E.A. The art of artificial intelligence, Themes and case studies in knowledge engineering, IJCAI5: 1014-1029.
[5] 董聪，郭晓华．智能计算中的热点问题．计算机科学，1999，26(4)：5-9．
[6] 王正志，薄涛．进化计算．北京：国防科技大学出版社．2000．
[7] 潘正君，康立山，陈毓屏．演化计算．北京：清华大学出版社1998．
[8] Wang Chuanlong, Xie Keming. Convergence of a new evolutionary computation algorithm in continuous state space. International Journal of Computer Mathematics, 2002, 79(1): 27-37.
[9] Goldberg D.E. Computer-aided Gas Pipeline Operation Using Genetic Algorithms and Rule Learning. Doctoral dissertation, University of Michigan, 1983.
[10] Goldberg D.E. Genetic Algorithms and Walsh Functions: Part Ⅰ, a Gentle Introduction. Complex Systems, 1989, 3: 129-152.
[11] Goldberg D.E. Genetic Algorithms and Walsh Functions: Part Ⅱ, Deception and its Analysis. Complex Systems, 1989, 3: 153-171.
[12] Goldberg D.E., Milman K, Tidd C. Genetic Algorithms: A Bibliography. IlliGAL Technical Report 920008,1992.
[13] Goldberg D.E. A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-oriented Simulated Annealing. Complex Systems, 1990, 4(4): 445-460.
[14] Goldberg D.E., Bridges C.L. An Analysis of a Reordering Operator on a GA-hard problem. Biological Cybernetics, 1990, 62: 397-405.
[15] Harik G.R., Lobo F.G, Goldberg D.E. The Compact Genetic Algorithms. IEEE Transactions on Evolutionary Computation, 1999, 3(4): 287-297.
[16] Goldberg D.E. Genetic Algorithms in Search. Optimization and Machine Leaning, Addison-Wesley, 1989.
[17] Kapur J.N., Kesavan H.K. Entropy Optimization Principles with Applications, Academic Press, 1992: 1-150.
[18] Kargupta H. Information Transmission in Genetic Algorithm and Shannon's Second Theorem, IlliGAL Report: http://gal4.ge.uiuc.edu/illigal.home.htm.
[19] Grefenstette J.J. Optimization of Control Parameters for Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics, 1986,16(1): 122-128.
[20] Michalewicz Z. Genetic Algorithms+Data Structures=Evolution Programs. Springer, 1996: 209-237.
[21] Michalewicz Z., D.Dasgupta, R.G.L.Riche, M.Schoenaurer. Evolutionary Algorithms for Constrained Engineering Problems. Computers and Industrial Engineering, 1996, 30(4): 851-870.
[22] Homaifar A., Qi X., Lai H. Constrained Optimization Via Genetic Algorithms. Simulation, 1994,62(4): 242-253.
[23] Coello C.A.C. Use of a Self-Adaptive Penalty Approach for Engineering Optimization Problems. Computers in Industry, 2000, 41:113-127.
[24] 潘正君，康立山，陈毓屏．演化计算．清华大学出版社，广西科学技术出版社，1998．
[25] 刘勇，康立山，陈毓屏．非数值并行算法(第二册：遗传算法)．北京，科学出版社，1997．
[26] 周红松．演化算法理论及其在有序问题中的应用，[硕士学位论文]．武汉，武汉大学，1995．
[27] 郭涛．模式演化算法及其在工作站群上的并行实现，[硕士学位论文]．武汉，武汉大学．1996．
[28] 何军．人工生态计算模型的研究，[博士学位论文]．武汉，武汉大学，1995．
[29] 潘正君．基于演化计算的并行问题求解，[博士学位论文]．武汉，武汉大学，1996．
[30] 陈国良，王煦法，庄镇泉，王东生．遗传算法及其应用．北京，人民邮电出版社，1996．
[31] 王小平，曹立明．遗传算法—理论、应用与软件实现．西安：西安交通大学山版社．2003．
[32] 曾建潮，查凯．用于求解数值优化问题的思维进化方法．见毛剑琴，曹希仁，第三届全球智能控制与自动化大会论文集，中国合肥，2000：126-128．
[33] Yan Gaowei, Xie Gang, Qiu Yuxia, etc. MEA Based Nonlinearity Correction Algorithm for the VCO of LFMCW Radar Level Gauge. The Tenth International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing (RSFDGrC 2005), August 31 st-September 3rd, 2005, University of Regina, Canada: 461-470.
[34] Sun Yah, Sun Yu, Sun Chengyi. Clustering and Reconstruction of Color images Using MEBML. In, Proc. of Inter. Conf. on Neural Networks & Brain, Beijing, China, 1998: 361-365.
[35] Sun Chengyi, Sun Yan Sun, Sun Yu. Model-Selection-Based Economic Prediction System Using MEBML. In, Proc. of IEEE Int. Conf. on Systems, Man, and Cybernetics, Tokyo, Japan, 1999: 628-633.
[36] Xie Gang, Gao Jinlan, Xie Keming. Fuzzy Modeling Based on Rough Sets and Mind Evolutionary Algorithm. In, Proc. of the Sixth International Conference on Electronic Measurement and Instruments, Taiyuan, China, 2003:109-112.
[37] 邱玉霞，谢刚，谢克明．基于思维进化算法的fuzzy控制系统没计与仿真．计算机科学，2005，32(8A)：112-114．
[38] Qiu Yuxia, Xie Gang, Xie Keming. Optimal Fuzzy Controller Design Based on Mind Evolutionary Algorithm. in, Proc. of 10th International Symposium On Integrated Circuits, Devices & Systems, Singapore, 2004:1124-1128.
[39] 郭刚，谢刚，谢克明．一种新的基于MEA的白适应模糊控制器．太原理工大学学报，2004，35(3)：247-250．
[40] 郝晓丽，谢克明．多级思维进化寻优的模糊PID控制器设计．太原理工大学学报，2004，35(2)：130-133．
[4l] 杜永贵，谢刚，谢克明．基于思维进化算法的模糊白适应控制系统．见，席裕庚，曹希仁，郭雷，第四届全球智能控制与自动化大会论文集，中国上海，2002：1887-1890．
[42] 邱玉霞，谢克明．一种优化模糊控制规则的新方法．太原理工大学学报，2004，35(3)：254-256．
[43] 韩晓霞，谢克明．基于思维进化算法的模糊自寻优控制．太原理工大学学报，2004，35(5)：523-525．
[44] 唐晓琪．基于思维进化算法的神经网络仿人智能控制策略在倒立摆系统中的研究，[硕士学位论文]．太原，太原理工大学，2003．
[45] Brady R M. Optimization strategies leaned from biological evolution. Nature, 317,804-806, 1985.
[46] Ftp://lumpi.informatik.uni-dortmund.de/pub/EA/paper/ea-app.ps.gz.
[47] Chun J. S, Jung H. K, HaHn S. Y. A study on comparison of optimization of performance between immune algorithm and other heuristic algorithms Magentics. 1998 34(5): 2972-2975.
[48] 孙承意，谢克明，程明琦，基于思维进化及其学习的框架及新进展，太原理工大学学报发，1999，30(5)：453-457．
[49] 王川龙，孙承意．基于思维进化的智能算法的收敛性研究．计算机研究与发展．2000，37(7)：838-842．
[50] Xie Keming, Mou Changhua, Xie Gang. A MEBML-based adaptive fuzzy logic controller. In, 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, 21st Century Technologies and Industrial Opportunities, October 22-28, 2000, Nagoya Congress Center, Nagoya, Japan: 1492-1496.
[51] 孙承意等，MEC应用于图像分析中的综述，计算机工程与应用，2004(31)：90-93．
[52] Pawlak Z. Rough sets. International journal of information and computer science, 1982, 11(5): 341～356.
[53] Pawlak Z. Rough sets: Theoretical Aspects of Reasoning about data. Boston: Kluwer Academic Publishers, 1991.
[54] Slowinski R. Intelligent Decision Support: Handbook of Application and Advances of the Rough Sets Theory, Kluwer Academic Publishers, 1992.
[55] 吴明芬．粗糙集理论的研究现状与前景．五邑大学学报(自然科学版)，2002，16(2)：16～21．
[56] Szladow A. Datalogic/R: Mining the knowledge in database. PC AI, 1993, 7(1), 40～41.
[57] Slowinski R. Rough set approach to decision analysis. AI Expert, March, 1995.
[58] Pawlak Z. Rough sets. Communications of ACM, November, 1995: 89～95.
[59] Bonikowski Z. Algebraic structures of rough sets, In: W. Ziarko, ed. Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag, 1994, 242～247.
[60] Bryniarski E. Formal description of rough sets. In: W. Ziarko, ed. Rough Sets, Fuzzy Sets and Knowledge Discovery, Springer-Verlag, 1994, 208～216.
[61] Lin T Y, Liu Q, Yao Y Y. Logic system for approximate reasoning: via rough sets and topology. Ras Z W, Zemankiva M eds. Methodologies for Intelligent Systems, Berlin: Springer-Verlag, 1994, 65～74.
[62] Yao Y Y, Lin T Y. Generalization of rough sets using modal Logics. Intelligent Automation and Soft Computing. 1996, 2(2): 103～120.
[63] Polkowski, L. T. On convergence of rough sets. Intelligent Decision support. Edited by Roman Slowinski. Kluwer, 1992, 305～311.
[64] 苗夺谦，王珏．粗糙集理论中知识粗糙性与信息熵关系的讨论．模式识别与人工智能，1998．11：34～40．
[65] 苗夺谦，王珏．粗糙集理论中概念与运算的信息表示．软件学报，1999，10：113～116．
[66] 张文修，吴伟志．粗糙集理论介绍和研究综述．模糊系统与数学，2000，14(4)：1～12．
[67] Lin T Y. A rough logic formalism for fuzzy controller: A hard and soft computing view. International Journal of Approximate Reasoning, 1996, 15: 395～414.
[68] Nakamura A. A rough logic based on incomplete information and its application. International Journal of Approximate Reasoning, 1996, 15: 367～378.
[69] Kuroki N. Rough ideals in semigroup. Information Science, 1997, 100: 139～163.
[70] Ziarko W. Variable precision rough sets model. Journal of Computer System Science, 1993, 46: 39～59.
[71] Katzberg, J D, Ziarko W. Variable precision rough sets with asymmetric bounds. Ziarko W P. Proceeding of RSKD' 93, Springer-Verlag, 1994, 167～177.
[72] Beynon Malcolm. Reducts within the variable precision rough sets model: A further investgation. European Journal of Operational Research, 2001, 134: 592～605.
[73] Beynon Malcolm J., Peel Michael J. Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega, 2001, 29(6): 561～576.
[74] 陈湘晖，朱善君，吉吟东，李永敏．扩展的粗糙集模型及其不确定性度量．清华大学学报(自然科学版)，2002，42(1)：35～38．
[75] Marzena Kryszkiewicz. Rough set approach to incomplete information systems. Information Science, 1998, 112: 39～49.
[76] Slowinski Roman, Vanderpooten Daniel. A Generalized Definition of Rough Approximations Based on Similarity. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(2): 331～336.
[77] Liang Jiye, Xu Zongben. Uncertainty measures of roughness of knowledge and rough sets in incomplete information systems. Intelligent Control and Automation, 2000. Proceedings of the 3rd World Congress on, 2000, vol. 4: 2526～2529.
[78] Wang Guoyin. Extension of rough set under incomplete information systems. Fuzzy Systems, 2002, vol.2: 1098～1103.
[79] Greco Salvatore, Matarazzo Benedetto, Slowinski Roman. Rough sets methodology for sorting problems in presence of multiple attributes and criteria. European Journal of Operational Research, 2002, 138: 247～259.
[80] 夏雨佳，李少远，席裕庚．一种基于粗糙集的信息系统决策规则获取方法．控制与决策，2001，16(5)：577～580．
[81] Lenarcik A., Piasta Z. Discretization of Condition Attributes Space Intelligent Decision Support. Edited by Roman Slowinski Kluwer, 1992, 373～389.
[82] 陈湘晖，朱善君，吉吟东．与特征选取和离散化集成的决策规则挖掘方法．系统工程理论与实践，2001，21(11)：1～7，30．
[83] Guan J. W., Bell D. A. Rough computational methods for information systems. Artificial intelligence, 1998, 105: 77～103.
[84] Guan J. W., Bell D. A.., Guan Z. Matrix computation for information systems. Information Science, 2001, 131: 129～156.
[85] Shan N., Ziarko W. An Incremental Learning Algorithm for Constructing Decision Rules. Rough Sets, Fuzzy Sets and Knowledge Discovery. Edited by Ziarko, W. Springer-Verlag, 1994, 326～334.
[86] Maamari E, Rajski J. The dynamic reduction of fault simulation. Computer-Aided Design of Integrated Circuits and Systems, 1993, 12(1): 137～148.
[87] Longshaw T., Haines S. Dynamic rough sets. Uncertainty Modeling and Analysis, 1995, and Annual Conference of the North American Fuzzy Information Processing Society, 1995, 292～295.
[88] Ziarko W. On Reduction of Knowledge Representation. Proc. Of the second International Sym. on Methodologies for Intelligent Systems, 1987, 99～113.
[89] Muraszkieqicz M., Rybinski H. Towards A parallel Rough Sets Computer. Rough Sets, Fuzzy Sets and Knowledge Discovery, Edited by Ziarko W, Springer-Verlag, 1994, 434～443.
[90] Lin T Y. Rough set theory in very large database. Proceedings of IMACS Multiconference, Lille, International Association for Mathematics and Computer in Simulation, 1996, vol. 2: 942～947.
[91] Nguyen S H, Skowron A., Synak P. Knowledge discovery in databases: Rough sets approach. Proc. of IFSA' 97, Prague, 1997, 204～209.
[92] Pawlak Z. Vagueness and uncertainty—a rough set perspective. Computation Intelligence, 1995, 11(2): 227～232.
[93] 陈奇南，梁洪峻．模糊集和粗糙集．计算机工程，2002，28(8)：138～140．
[94] Dubois D., Prade H. Rough fuzzy sets and fuzzy rough sets. International Journal of General system, 1990, 17: 191～209.
[95] Mohua Banerjee, Sankar K. P. Roughness of a fuzzy set. Infonnatics and Computer Science, 1996, 93: 235～246.
[96] Hashemi Ray R., Charlese. Eppeson, The integration of rough sets and neural network paradigms in predictive systems. Intelligent Engineering systems through artificial neural networks, 1999, 9: 499～504.
[97] Khoo Li-Pheng, Zhai Lian-Yin. A prototype genetic algorithm-enhanced rough set-based rule induction system. Computers in Industry, 2001, 46(1): 95～106.
[98] Zhai Lian-Yin, Khoo Li-Pheng, Fok Sai-Cheong. Feature extraction using rough set theory and genetic algorithms—an application for the simplification of product quality evaluation. Computers and Industrial Engineering, 2002, 43(4): 661～676.
[99] Chiu T., Lin T. Y. Tuning rough controllers by genetic algorithms. Soft Computing in Intelligent Systems and Information Processing, 1996, 326～331.
[100] 熊范纶，杭小树．基于粗糙集理论的示例学习研究．浙江大学学报(理学版)，2002，29(3)：346～354．
[101] 周东华，王文辉．基于遗传算法的一种粗糙集知识约简算法．系统仿真学报，2001，13(suppl)：91～93，96．
[102] 李玉榕．信息融合与智能处理的研究，博士学位论文．浙江大学，杭州，2001．
[103] 王熙法，王上飞，陈恩红，胡静．交互式遗传算法中收敛性及用户评估质量的提高．中国科学技术大学学报．，2002，32(2)：210～216．
[104] Lingras P., Davies C. Rough Genetic Algorithm. Zhong N, Skowron A eds. Proc 7th Intl Wksp on RSFD, Springer, 1999, 38～46.
[105] 刘健勤，刘其兴．一种矿井涌水量预测的新的进化计算算法．煤炭学报，1998，23(1)：107～200．
[106] Rival I. Ordered sets. Berlin: Reidel, 1982.
[107] 邹先霞，杜威等．一种基于粗集理论的概念格构造方法．华中师范大学学报(自科版)，2001，35(2)：146～149．
[108] Kent R E. Rough concept analysis. In: Ziarko W P ed. Rough Sets, and Fuzzy Sets and Knowledge Discovery (RSKD' 93), London: Springer-Verlag, 1994, 248～255.
[109] 胡可云，陆玉昌，石纯一．概念格及其应用进展．清华大学学报(自然科学版)，2000，40(9)：77～81．
[110] Wong S., Lingras P. The compatibility view of Shafer-Dempster theory using the concept of rough set. Methodologies for Intelligence Systems, 1989, 4: 33～42.
[111] Skowron A., et al. From rough set theory to evidence theory. In: Advances in the Dempster-Shafer theory of evidence, John Wiley & Sons, Inc., New York, 1994, 193～236.
[112] Yahia M. E., Mahmod R., Sulaiman N., Ahmad F. Rough neural expert systems. Expert Systems with Applications, 2000, 18(2): 87～99.
[113] Pawlak Zdzis, Iz. xl aw. Rough sets, decision algorithms and Bayes' theorem, European Journal of Operational Research, 2002, 136(1): 181～189.
[114] Pawlak Zdzis, lz. shtsls aw. Rough sets and intelligent data analysis. Information Sciences, 2002, 147(1-4): 1～12.
[115] Grzymala-Busse J. W.. LERS—A sustem for leafing from examples based on rough sets. In "Intelligent Decision Support: Handbook of Application and Advances of the Rough Sets Theory" edited by R. Slowinski, 1992.
[116] Mrozek A., et al. Methodology of Rough Controller Synthesis. IEEE International Conference on Fuzzy Systems, NJ, USA, 1996, 2: 1135～1139.
[117] Adam MROZEK. Use of Rough Sets and Decision Tables for Implementing Rule-Based Control of Industrial Processes. Computer and Infonnati0n Science, 1986, 34(5-6): 357～370.
[118] 谢克明，杨静．粗糙集理论及其在智能控制领域的应用前景．太原理工大学学报，1999，30(4)：338～342．
[119] 马志锋，刑汉承，郑晓妹．决策表中的不确定性研究．控制与决策，2000，15(6)：703～707．
[120] Nejman D. A rough set based method of handwritten numerals classification. Institute of Computer Science Reports, 18/94, Warsaw University of Technology, 1994.
[121] Swiniarski Roman W., Hargis Larry. Rough sets as a front end of neural-networks texture classifiers. Neurocomputing, 2001, 36(1-4): 85～102.
[122] Wojcik Z. M. Application of rough sets for edge enhancing image filters. Image Processing(IEEE International Conference), 1994, vol.2: 525～529.
[123] Mohabey A., Ray A. K. Rough set theory based segmentation of color images. Fuzzy Information Processing Society, NAFIPS, 2000: 338～342.
[124] 杨锦堂，刘美林，王慧敏，徐立中．粗糙集理论在图像增强中的应用．数据采集与处理，1999，14(3)：307～310．
[125] 杨锦堂，王慧斌，徐立中．基于粗糙集理论的图像增强方法．仪器仪表学，2000，21(5)：514～515，524．
[126] 徐立中，王慧敏．一种图象预处理的新算法．中国图象图形学报，1999，4(11)：911～915．
[127] Ahn B. S., Cho S. S., Kim C. Y.. The integrated methodology of rough set theory and artificial neural network for business failure prediction. Expert Systems with Applications, 2000, 18: 65～74.
[128] Jelonek J., et al. Rough set reduction of attributes and their domains for neural networks. Computational Intelligence, 1995, 11(2): 339～347.
[129] Chen-Jimenez I. E., Kornecki A. et al. Software safety analysis using rough sets. Southeastcon '98, Proceedings of IEEE, 1998: 15～19.
[130] Lewis T., Perkowski M., Jozwiak L. Learning in hardware: architecture and implementation of an FPGA-based rough set machine. EUROMICRO Conference, 1999, vol. 1: 326～334.
[131] 管天云．多传感器信息融合研究，博士学位论文．浙江大学，杭州，1998．
[132] 张启忠，罗志增．粗集理论在信息融合中的应用．机电工程，2001，18(5)：29～31．
[133] Fernandez-Baizan M.C., Ruiz E.M., Sanchez J. M. Using RSDM to mine Socio-Economic Indicators. Proc. of 1997 IEEE International Conference on Computational Cybernetics and Simulation, 1997, 2: 1385-1389.
[134] Dimitras A. I., Slowinski R., Susmaga R., Zopounidis C. Business failure prediction using rough sets. European Journal of Operational Research, 1999, 114(2): 263-280.
[135] Tay Francis E.H., Shen Lixiang. Economic and financial prediction using rough sets model. European Journal of Operational Research, 2002,141 (3): 641～659.
[136] Komorowski Jan, 0hrn Aleksander. Modelling prognostic power of cardiac tests using rough sets. Artificial Intelligence in Medicine, 1999, 15(2): 167～191.
[137] Gorzalczany Marian B., Piasta Zdzislaw. Neuro-fuzzy approach versus rough-set inspired methodology for intelligent decision support. Information Sciences, 1999, 120(1-4): 45-68.
[138] Teghem J., et al. Use of "rough sets" method to draw premonitory factors for earthquakes by emphasizing gas geochemistry: the case of a low seismic activity content in Belgium. Intelligent decision support: handbook of applications and advances of the rough sets theory, 1992, 165-179.
[139] R. Deja. Conflict model with negotiations. Institute of Computer Science Reports, 51/95, Warsaw University of Technology, 1995.
[140] Nurmi Hannu, Kacprzyk Janusz, Fedrizzi Mario. Probabilistic, fuzzy and rough concepts in social choice. European Journal of Operational Research, 1996, 95(2): 264-277.
[1] Holland J H. Adaptation in natural and artificial systems [M]. Ann Arbor: University of Michigen Press, 1975.
[2] Fogel L J, Owens A J, Walsh M J. Artificial intelligence through simulated evolution [M]. New York: John Wiley, 1966.
[3] Forgel D B. An introduction to simulated evolutionary optimization. IEEE Transactions on Neural Networks, 1994, 5(1): 85-95.
[4] 徐宗本，李国．解全局优化问题的仿生类算法(Ⅰ)——模拟进化算法．运筹学杂志，1995，14(2)：1-13．
[5] Michelewicz Z. Genetic Algorithms+Data Structure=Evolutionary Programs, 3rd ed. New York: Springer-Verlag, 1996.
[6] Dawid H A. A Markov chain analysis of genetic algorithms with a state dependent fitness function. Complex Systems, 1994, 8: 407-417.
[7] Mahfoud S W. Finite Markov chain models of an alternative selection strategy for the genetic algorithm. Complex Systems, 1993, 7: 155-170.
[8] Rudolph G. Convergence analynsis of canonical genetic algorithms. IEEE Tans Neural Networks, 1994, 5(1): 96-101.
[9] Suzuki J. A further result on the Markov chain model of genetic algorithms and its application to a simulated annealing-like strategy. IEEE Trans System, Man&Cybernetics, 1998, 28(1): 95-102.
[10] Nix A. E, Vose D M. Modeling genetic algorithms with Markov chains. Annals of Mathematics and Artificial Intelligence, 1993, 5: 79-88.
[11] Vose M D, Liepins G E. Punctuated equilibrium in genetic search. Complex Systems, 1991, 5: 31-44.
[12] Cerf R. The dynamoics of mutateon-selection algorithms with large population sizes. Annals of Institute Henri Poicare, 1996, 32(4): 455-508.
[13] Cerf R. Asymptotic convergence of genetic algorithms. Advances in Application Probability, 1998, 30(2): 5221-550.
[14] Davis T E; Principe J C. A simulated annealing like convergence theory for the simple genetic algorithm. In: Proc the 4th Int. Conf. On Genetic Algorithms, San Mateo, CA, 1991: 174-181.
[15] Francois O. Convergence in simulated evolutionary algorithms. Complex Systems, 1996, 10: 311-319.
[16] Peck C C, Dhawan A P. Genetic algorithms as global random search methods: An alterative perspective. Evolutionary computation, 1995, 3(1): 39-80.
[17] 潘正君，康立山等．演化计算．清华大学出版社，桂林：广西科学技术出版社，1998．
[18] Goldberg D E. Genetic Algorithms in Search, Optimization and Machine Learning. Addison Wesley, Reading, MA, 1989.
[19] J. H. Holland. Adaptation in Natural and Artificial Systems. Ann Arbor: Univ. Of Michigan Press, 1975.
[20] J. R. Koza. Genetic Programming: on the Programming of Computers by Means of Natural Selection. MIT Press, 1992.
[21] L. J. Fogel. Artificial Intelligence Throurgh Simulated Evolution. New York: John Wiley, 1996.
[22] Rechenberg. Cybernetic Solution Path of an Experimental Problem. Roy Aircr, Establ, Libr. Trans. 1222, Hants, U. K. Farnboroungh, 1965.
[23] 张涛．热力系统在线进化优化体系研究．博士学位论文．北京：清华大学2000．
[24] 王鑫鑫，徐向东．进化优化算法通用框架模型研究．电站系统工程，2002，1886：1～4．
[25] 王鑫鑫，徐向东．分层协同进化模型在热力系统在线优化中的应用研究．热能动力工程2002，174：339～341．
[26] 云庆夏．进化算法．北京：冶金工业出版社2000．
[27] 王小平，曹立明．遗传算法理论应用与软件实现．西安：西安交通大学出版社2002
[28] 云庆夏，黄光球，王战权．遗传算法和遗传规划一种搜索寻优技术．北京：冶金工业出版社．1997．
[29] 王正志，薄涛．进化计算．长沙：国防科技大学出版社，2000．
[30] Goldberg E. Genetic Algorithms in Search, Optimization and Machine Leaming. Reading, MA: Addison Wesley, 1989.
[31] Koza J. R. Genetic Programming: A paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems. Report No. STAN-CS-90-1314, Stanford University, 1990．
[32] Yao X, Liu Y. Fast evolutionary strategies[A]. P. J. Angeline, ed. Proceedings of the Sixth Annual Conference on Evolutionary Programming[C]. Berlin: Springer, 1997, 151161.
[33] Yang Y., Yang P. C., Huang C. L. Evolutionary Programming Based Economic Dispatch for Units with Non-smooth Fuel Cost Functions. IEEE PES Winter Power Meeting, USA, 1995.
[34] 张文修，梁怡．遗传算法的数学基础．西安：西安交通大学出版社，2000．
[35] 刘健勤．人工生命理论及其应用．北京冶金工业出版社．
[36] 赵达纲，朱迎善．应用随机过程．北京机械工业出版社，1990．
[37] 龚怀云，寿纪麟，王绵森，应用泛函分析，西安交通大学出版社，1995．
[38] Michalewicz, Z., Genetic Algorithms+Data Structure=Evolutionary Programs, Springer-Verlag, Berlin, Heidelberg, 2nd Edition, 1994.
[39] Rudolph, G. Convergence Analysis of Canonical Genetic Algorithms, IEEE Trans. On N. N. Vol. 5, No. 1, 1994.
[40] 刘健勤，魏敏洁．进化计算的可计算性．计算技术与自动化，Vol．17，No．3，1998，1-2．
[1] 蔡自兴，徐光祐．人工智能及其应用，第三版．北京：清华大学出版社，2004．
[2] Holland J H. Adaptation in nature and artificial system. Ann Arbor: University of Michigan Press, 1975: 35.
[3] Bethke A D. Genetic algorithms as function optimizers. Dissertation Abstracts International, 1980, 4(19): 48.
[4] Goldberg D E. Simple Genetic Algorithms and the Minimal, Deceptive Problem. In: L. Davis, Genetic Algorithms and Simulated Annealing, San Mateo, CA, Morgan Kaufman publisher, 1987: 74-88.
[5] Eshelman L J. The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination. In: Gregory J E Rawlins. Foundations of Genetic Algorithms, San Mateo, CA, Morgan Kaufman publisher, 1991: 265-283.
[6] Louis S J, Rawlins G J E. Syntactic Analysis of Convergence in Genetic Algorithms. In: L Darrell Whitley, Foundations of Genetic Algorithms2, San Mateo, CA, Morgan Kaufmann publisher, 1993: 141-151.
[7] Whitley D, Hanson T. Optimizing neural networks using faster, more accurate genetic search. In: J. David Schaffer, Proceeding of the International Conference on Genetic Algorithms, George Mason University, Fairfax, Virginia, USA, June 1989: 391-396.
[8] Whitley D, Starkeather T, Bogart C. Genetic algorithms and neural networks: optimizing connection and connectivity. Parallel Computing, 1990, 14: 247-361.
[9] Yoon B, Holmes D J. Efficient genetic algorithms for training layered feed forward neural networks. Information Sciences, 1994,76(12): 67-85.
[10] Bhanderi D, Pal H R, Pal S K. Directed mutation in genetic algorithms. Information Sciences, 1994, 79(3): 251-270.
[11] Tsutsui S, Ghosh A, Corne D, et al. A Real Coded Genetic Algorithm with an Explorer and an Exploiter Populations. In: Proc. of the 7th International Conference on Genetic Algorithms, San Francisco, 1997: 238-245.
[12] Sun Zennqi, Wan Qian. A Modified Genetic Algorithm: Meta-Level Control of Migration in a Distributed GA. In: Proc. of IEEE International Conference on Evolutionary Computation, Piscataway, 1995
[13] Sun Chengyi, Sun Yan, Wei Lijun. Mind-Evolution-Based Machine Learning: Frame work and the Implementation of Optimization. In: Proc. of IEEE International Conference on Intelligent Engineering Systems, Vienna, Austria, 1998, 355-359.
[14] 孙承意，谢克明，程明琦．基于思维进化机器学习的框架及新进展．太原理工大学学报，1999，30(5)：453-457．
[15] Wang Chuanlong, Xie Keming. Convergence of a new evolutionary computation algorithm in continuous state space. International Journal of Computer Mathematics, 2002, 79(1): 27-37.
[16] 王川龙，孙承意．基于思维进化的MEBML算法的收敛性研究．计算机研究与发展，2000，37(7)：838-842．
[17] 孙承意，孙岩，谢克明．思维进化——高效率的进化计算方法．第三届全球智能控制与自动化大会论文集，中国合肥，2000：112-117．
[18] Wei Lijun, Sun Yan, Sun Chengyi. A more efficient similartaxis strategy of MEBML. In: Proc. of the IASTED Conf. on Modeling and Simulation, Pittsburgh, Pennsylvania, USA, Acpa Press, 1999: 5-8.
[19] 曾建潮．基于思维进化机器学习的自适应趋同策略．计算机工程与科学，2000，22(4)：95-107．
[20] 曾建潮，查凯．基于思维进化机器学习的异化策略研究．第三届全球智能控制与自动化大会论文集，中国合肥，2000：129-131．
[21] Sun Chengyi, Sun Yan. The Performance of a Modified MEBML System in Noisy Environment. In: Proc. of IEEE Intl. Conf. on Systems, Man and Cybernetics, Tokyo, Japan, 1999: 168-171.
[22] 曾建潮，查凯．用于求解数值优化问题的思维进化方法．第三届全球智能控制与自动化大会论文集，中国合肥，2000：126-128．
[23] Sun Yan, Sun Yu, Sun Chengyi. Clustering and Reconstruction of Color images Using MEBML. In: Proc. of Intl. Conf. on Neural Networks & Brain, Beijing, China, 1998: 361-365.
[24] Sun Chengyi, Sun Yan Sun, Sun Yu. Model-Selection-Based Economic Prediction System using MEBML. In: Proc. of IEEE Intl. Conf. on Systems, Man, and Cybernetics, Tokyo, Japan, 1999.
[25] Xie Gang, Gao Jinlan, Xie Keming. Fuzzy Modeling Based on Rough Sets and Mind Evolutionary Algorithm. In: Proc. of the Sixth International Conference on Electronic Measurement and Instruments, Taiyuan, China, 2003:109-112.
[26] 邱玉霞，谢刚，谢克明．基于思维进化算法的fuzzy控制系统设计与仿真．计算机科学，2005，32(8A)：112-114．
[27] Qiu Yuxia, Xie Gang, Xie Keming. Optimal Fuzzy Controller Design Based on Mind Evolutionary Algorithm. In: Proc. of 10th International Symposium On Integrated Circuits, Devices & Systems, Singapore, 2004: 1124-1128.
[28] 郭刚，谢刚，谢克明．一种新的基于MEA的自适应模糊控制器．太原理工大学学报，2004，35(3)：247-250．
[29] 郝晓丽，谢克明．多级思维进化寻优的模糊PID控制器设计．太原理工大学学报，2004，35(2)：130-133．
[30] 杜永贵，谢刚，谢克明．基于思维进化算法的模糊自适应控制系统．第四届全球智能控制与自动化大会论文集，中国上海，2002：1887-1890．
[31] 邱玉霞，谢克明．一种优化模糊控制规则的新方法．太原理工大学学报，2004，35(3)：254-256．
[32] 韩晓霞，谢克明．基于思维进化算法的模糊自寻优控制．太原理工大学学报，2004，35(5)：523-525．
[33] 唐晓琪．基于思维进化算法的神经网络仿人智能控制策略在倒立摆系统中的研究．[硕士学位论文]，太原，太原理工大学，2003．
[34] Xie Keming, Mou Changhua, Xie Gang. A MEBML-based adaptive fuzzy logic controller. In: 2000 IEEE International Conference on Industrial Electronics, Control and Instrumentation, 21st Century Technologies and Industrial Opportunities, October 22-28, 2000, Nagoya, Japan: 1492-1496.
[35] Xie Keming, Mou Changhua, Gang Xie, et al. A new MEBML-based algorithm for adjusting parameters on-line. In: Proc. of 5th International Conference on Signal Processing, Beijing, 2000: 1714-1717.
[36] Xie Keming, Mou Changhua, Xie Gang. The multi-parameter combination mind-evolutionary-based machine learning and its application. Proceedings. of 2000 IEEE International Conference on Systems, Man, and Cybernetics, Music City, Tennessee, USA, 2000: 183-187.
[37] 谢克明，杜永贵，孙承意．基于思维进化机器学习算法在水泥生料配比中的应用．第三届全球智能控制与自动化大会论文集，中国 合肥，2000：132-134
[38] Shannon CE. The mathematical Theory of Communication [M]. Urbana: University of Illinois Press, 1992.
[39] Lee Jooyoung. New Monte Carlo algorithm: entropic sampling[J]. Physical Review Let, 1993, 71(1): 211214.
[40] Lee Chang Yong, Han Seung Kee. Evolutionary optimization algorithm by entropic sampling[J]. Physical Review E, 1998, 57(3): 36113617.
[41] 需庆生，信息论[M]．西安：西安交通大学出版社，1989．
[42] 龚怀云，寿纪麟，王绵森，应用泛函分析，西安交通大学出版社，1995，10-16．
[43] Michalewicz, Z., Genetic Algorithms+Data Structure=Evolutionary Programs, Springer-Verlag, Berlin, Heidelberg, 2nd Edition, 1994.
[44] Rudolph, G., Convergence Analysis of Canonical Genetic Algorithms, IEEE Trans. On N.N., Vol.5, No. 1, 1994.
[45] Michalewicz, Z. Genetic Algorithms+Data Structures=Evolution Programs. Berlin, Heidelberg, New York: Springer-Verlag, 1992.
[46] http://agat.ely.pg.gda.pl/～mkwies/dyd/sro/geadocu/fcnfun1b.html.
[47] 周明，孙树栋．遗传算法原理及应用，北京：国防工业出版社，2002．
[48] 潘正君，康立山，陈毓屏．演化计算，北京：清华大学出版社，1998．
[1] Pawlak Z. Rough sets: Theoretical Aspects of Reasoning about data. Boston: Kluwer Academic Publishers, 1991
[2] Duntsch I., Gediga G.. Uncertainty measures of rough set prediction. Artificial Intelligence, 1998, 106: 109-137.
[3] Pawlak Z. Rough set theory and its applications to data analysis. Cybernetics and Systems, 1998, 29: 661～688.
[4] Hu X. H., Cercone N. Learning In Relational Databases: A Rough Set Approach. International Journal of Computational Intelligence, 1995, 11(2): 323～338.
[5] 王国胤．Rough集理论与知识获取．西安交通大学出版社，西安，2001．5．
[6] Pawlak Z., Rough Sets, Theoretical Aspects of Reasoning about Data, Nowowiejska 15/19, Warsaw, Poland, 1990.
[7] Skowron A., Rauszer C. The discernibility matrices and functions[M]. In intelligent decision support. Handbook of applicastion and advancees of the rough set theory. Ed. R. Slowinski, 331-362. Boston: Kluwer Academic Publishers, 1991.
[1] 孙即祥．图像处理．北京：科学出版社．2004．
[2] 蔡自兴，徐光祐．人工智能及其应用，第三版．北京：清华大学出版社，2004．
[3] Pawlak Z. Rough sets. International journal of information and computer science, 1982, 11(5): 341～356.
[4] Pawlak Z. Rough sets: Theoretical Aspects of Reasoning about data. Boston: Kluwer Academic Publishers, 1991.
[5] 王正志，薄涛．进化计算．北京：国防科技大学出版社．2000．
[6] 潘正君，康立山，陈毓屏．演化计算．北京：清华大学出版社，1998．
[7] 薛景浩，章毓晋，林行刚．SAR图像基于Rayleigh分布假设的最小误差阈值化分割．电子科学学刊，1999，21(2)：219～2252．
[8] 陈哲，冯天谨．基于小波分形特征提取的图像分割方法．中国图象图形学报，1999，4A(12)：1072～1077．
[9] Tian B, Shaikh M A, Azimi Sadjadi M R, etal. A Study of Cloud Classification with Neural Networks Using Spectral and Textural Features. IEEE Trans. Neural Networks, 1999,10(1):138～151.
[10] Zhang Y, Smith S M, and Brady J M. Segmentation of brain MR images using Markov Random Field. Proceedings of Medical Image Understanding and Analysis' 99, available on http://www.ipg.umds.ac.uk/miua/.
[11] Mari Pierre Dubuisson Jolly, Sridhar Lakshmanan, and Anil K. Jain. Vehicle Segmentation and Classification Using Deformable Templates. IEEE Trans. Pattern Analysis and Maching Intelligence, 1996,18(3):293～308.
[12] Rob Koenen. MPEG4 Overview. Available on http://drogo.cselt.it/mpeg/standards/mpeg4/mpeg4.html.
[13] Swanson MD, Tewfik A H. Fast Progressively Refined Image Retrieval. Journal of Electronic Imaging, 1998, 7(3):443～452.
[14] 王爱民，沈兰荪．图像分割研究综述．测控技术，2000，19(5)：1-6．
[15] 姚敏等．数字图像处理．北京：机械工业出版社，2006．1．
[16] 章毓晋，图像工程(中册)图像分析(第2版)，北京：清华大学出版社，2003．
[17] Kenneth R．Castleman著，朱志刚等译．数字图像处理(Digital Image Processing)．北京：电子工业出版社， 2002．
[18] 边肇祺，张学工等．模式识别(第二版)．北京：清华大学出版社，2000．
[19] Costa L F, Cesar R M. Shape Analysis and Classification: Theory and Practice. CRC Press, 2001.
[20] ASM International. Practical Guide to Image Analysis. ASM International, 2000.
[21] 王国胤．Rough集理论与知识获取．西安：西安交通大学出版社，2001．5．
[22] 潘自强，辐射防护的现状与未示来，北京：原子能出版社，1997，218-230．