免疫克隆选择算法应用研究
|
摘要
在人工免疫系统研究中,许多方法都借用了克隆选择的思想,或在实现过程中嵌入克隆选择机制。而克隆选择原理的应用研究尚不完善,许多免疫性质只是隐喻使用,并没有真正在人工系统中得到实现。本课题完善了人工免疫系统中的克隆选择机制及算法,研究其在函数优化、PID控制、免疫调度、聚类问题中的应用,论文完成的主要工作如下:
基于免疫网络理论用于解释抗体克隆过程中的浓度调节现象,提出在种群进化初期和末期应该按照不同的进化策略进行抗体克隆的算法—免疫网络克隆优化算法(INCOA),它可以增大种群进化末期抗体多样性,避免算法早熟,并采用非线性方程组和多峰值函数对算法进行测试,测试结果证实了算法的有效性。
提出简单克隆选择算法(SCSA)用于控制系统PID控制参数优化,通过倒立摆、一阶延迟函数模型、六阶函数模型的MATLAB仿真实验,以及倒立摆实时控制实验证明:算法实现简单,计算复杂性小;对于目标函数解的搜索结果稳定,与基本克隆选择算法(BCSA)相比算法收敛速度明显提高;SCSA用于解决PID控制参数优化问题,整体性能优于遗传算法和BCSA。
将小生境理论用于自适应克隆启发算法的设计,提出自适应克隆启发算法(SACHA)和小生境自适应克隆启发算法(NSACHA)用于复杂的作业调度问题。动态模仿免疫细胞之间的自适应和协同进化行为,表现为不同种群之间、不同抗体之间对抗体克隆数量的竞争,改善了问题解(抗体)对问题空间的覆盖率,提高算法全局搜索能力。NSACHA能同时求得全局最优值和局部最优值,能够对得到的结果进行次优性的定量评估,在实际调度问题中具有一定的现实意义。
最后,针对聚类问题提出抗体记忆克隆聚类算法(AMCCA),利用克隆选择、克隆记忆等免疫机制对抗原数据产生记忆细胞,抑制较差抗体,从而达到将数据压缩的目的。实验证明在搜索空间进行随机搜索的AMCCA比传统算法能发现更好的解,与其它聚类算法相比性能接近,AMCCA可以用于聚类。
本文分析研究结果表明基于生物克隆选择原理及机制启发的免疫克隆选择算法,能够获得等同或优于传统方法和其它进化方法的性能,具有一定的实际应用价值。
In the field of Artificial Immune System, many methods adapted the theory of clone selection, or embedded the mechanism in the process of realization. But the application of clone selection principle is not perfect. Many characteristics are only applied by metaphor. They are not realized in practice. In the paper, clone selection algorithm is improved. It is applied in function optimization, PID control, Job shopping, clustering. The main work is as follows:
Based on the concentration regulation in the process of antibody cloning, which is explained by immune network theory, it presents different evolution strategies at the beginning and at the end of evolution. Immune network clone optimization(INCOA) is presented. It can increase the diversity of antibody population at the end of evolution. Thus it can avoid premature. It is tested by non-linear equation sets and multi-peaks function. The test results show the validity of the algorithm.
Simple clone selection algorithm(SCSA) is presented to optimize the parameters of PID control system. Through the experiments, including pendulum, one-order delay function model, six-order function model, and the real time control of pendulum, the results show that it is simple and has less computing complexity. It is stable for searching the solution of object function. Its convergence speed is clearly higher than that of basic clone selection algorithm(BCSA).SCSA is used to solve the problem of optimizing the parameters of PID. Its whole performance is better than those of GA and BCSA.
The niching theory is used to design the self-adaptive clone heuristic algorithm. Self -adaptive clone heuristic algorithm(SACHA) and niching self-adaptive clone heuristic algorithm(NSACHA) are presented to the problem of Job shop. It simulates the self-adaptation and co-evolution among immune system. It expresses to be the competition to the amount of antibodies among different population, different antibodies. It improves the cover rate to the problem space and enhance the global searching ability. NSACHA can get the global optimization solution and local optimization solution at the same time. So it has real meaning in practice.
At last, for the problem of clustering, antibody memory clone clustering algorithm(AMCCA) is proposed. It uses clone selection, clone memory to produce memory cells and suppresses the worse antibodies. Thus, it can reach the aim of suppressing data. The test results show that random search AMCCA can find better solution than traditional algorithms. Its performance is close to the other typical clustering algorithm. So it can be used to cluster.
The analysis results show that clone selection algorithm based on biology clone selection principle has equal or better performance with other typical methods. To some extent, it has real application value in practice.
基于免疫网络理论用于解释抗体克隆过程中的浓度调节现象,提出在种群进化初期和末期应该按照不同的进化策略进行抗体克隆的算法—免疫网络克隆优化算法(INCOA),它可以增大种群进化末期抗体多样性,避免算法早熟,并采用非线性方程组和多峰值函数对算法进行测试,测试结果证实了算法的有效性。
提出简单克隆选择算法(SCSA)用于控制系统PID控制参数优化,通过倒立摆、一阶延迟函数模型、六阶函数模型的MATLAB仿真实验,以及倒立摆实时控制实验证明:算法实现简单,计算复杂性小;对于目标函数解的搜索结果稳定,与基本克隆选择算法(BCSA)相比算法收敛速度明显提高;SCSA用于解决PID控制参数优化问题,整体性能优于遗传算法和BCSA。
将小生境理论用于自适应克隆启发算法的设计,提出自适应克隆启发算法(SACHA)和小生境自适应克隆启发算法(NSACHA)用于复杂的作业调度问题。动态模仿免疫细胞之间的自适应和协同进化行为,表现为不同种群之间、不同抗体之间对抗体克隆数量的竞争,改善了问题解(抗体)对问题空间的覆盖率,提高算法全局搜索能力。NSACHA能同时求得全局最优值和局部最优值,能够对得到的结果进行次优性的定量评估,在实际调度问题中具有一定的现实意义。
最后,针对聚类问题提出抗体记忆克隆聚类算法(AMCCA),利用克隆选择、克隆记忆等免疫机制对抗原数据产生记忆细胞,抑制较差抗体,从而达到将数据压缩的目的。实验证明在搜索空间进行随机搜索的AMCCA比传统算法能发现更好的解,与其它聚类算法相比性能接近,AMCCA可以用于聚类。
本文分析研究结果表明基于生物克隆选择原理及机制启发的免疫克隆选择算法,能够获得等同或优于传统方法和其它进化方法的性能,具有一定的实际应用价值。
In the field of Artificial Immune System, many methods adapted the theory of clone selection, or embedded the mechanism in the process of realization. But the application of clone selection principle is not perfect. Many characteristics are only applied by metaphor. They are not realized in practice. In the paper, clone selection algorithm is improved. It is applied in function optimization, PID control, Job shopping, clustering. The main work is as follows:
Based on the concentration regulation in the process of antibody cloning, which is explained by immune network theory, it presents different evolution strategies at the beginning and at the end of evolution. Immune network clone optimization(INCOA) is presented. It can increase the diversity of antibody population at the end of evolution. Thus it can avoid premature. It is tested by non-linear equation sets and multi-peaks function. The test results show the validity of the algorithm.
Simple clone selection algorithm(SCSA) is presented to optimize the parameters of PID control system. Through the experiments, including pendulum, one-order delay function model, six-order function model, and the real time control of pendulum, the results show that it is simple and has less computing complexity. It is stable for searching the solution of object function. Its convergence speed is clearly higher than that of basic clone selection algorithm(BCSA).SCSA is used to solve the problem of optimizing the parameters of PID. Its whole performance is better than those of GA and BCSA.
The niching theory is used to design the self-adaptive clone heuristic algorithm. Self -adaptive clone heuristic algorithm(SACHA) and niching self-adaptive clone heuristic algorithm(NSACHA) are presented to the problem of Job shop. It simulates the self-adaptation and co-evolution among immune system. It expresses to be the competition to the amount of antibodies among different population, different antibodies. It improves the cover rate to the problem space and enhance the global searching ability. NSACHA can get the global optimization solution and local optimization solution at the same time. So it has real meaning in practice.
At last, for the problem of clustering, antibody memory clone clustering algorithm(AMCCA) is proposed. It uses clone selection, clone memory to produce memory cells and suppresses the worse antibodies. Thus, it can reach the aim of suppressing data. The test results show that random search AMCCA can find better solution than traditional algorithms. Its performance is close to the other typical clustering algorithm. So it can be used to cluster.
The analysis results show that clone selection algorithm based on biology clone selection principle has equal or better performance with other typical methods. To some extent, it has real application value in practice.
引文
[1] Jerne N K. Towards a network theory of the immune system. Annual Immunology, 1974, 125C, 373-389P
[2] Farmer J D., Packard N H. ,Perelson A. S. The immune system, adaptation,and machine learning, Physica, 1986, 22D, 187-204P
[3] Varela F J, Stewart J. Dynamics of a class of immune network. Global Stability of Idiotype Interactions. J. Theoretical Biology, 1990, 144(1):93-101P
[4] Perelson A S. Immune network theory. Immunological review 1989, 10:5-36P
[5] Bersini H. , Varela FJ. Hints for Adaptive Problem Solving Gleaned from Immune Networks. Proc of the First Workshop on Parallel Problem Solving from Nature, 1990:343-354P
[6] Ishida Y. Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model. Proc. of IJCNN 90, San Diego, 1990
[7] Forrest S. , Perelson A. , Allen L. , Cherukuri R. Self-nonself discrimination in a computer. Proc. of the IEEE Symposium on Research in Security and Privacy, 1994: 202-212P
[8] Cooke D E. , Hunt J E. Recognizing promoter sequences using an artificial immune system. Proc. Intelligent Systems in Molecular Biology (ISMB'95),Pub AAAI Press, 1995: 89-97P
[9] Hunt J E., Cooke D E. Learning using an artificial immune system. Journal of Network and Computer Applications. 1996, 19: 189 -212P
[10] Burnet F M. The clonal selection theory of acquired immunity. Cambridge University Press, 1959
[11] De Castro L N. , Von Zuben F J. Artificial immune systems:: part I - basic theory and applications.Technical Report- RT DCA 01/99,89,1999
[12]Dasgupta D.,Forrest S.Artificial immune systems in industrial applications.Proc.of the IPMM'99,1999
[13]Timmis J.,Neal M.,Hunt J.An artificial immune system for data analysis.Biosystems,2000,55(1/3):143-150P
[14]De Castro L N.,Timmis J.Artificial immune systems:a new computational intelligence approach.Springer-verlag,Berlin Heidelberg,2002
[15]De Castro L N.,Von Zuben F J.Learning and optimization using the clonal selection principle.IEEE Transactions on Evolutionary Computation.2002,6(3):239-251P
[16]Joanne H.,Walker Simon M G.Dynamic function optimization::comparing the performance of clonal selection and evolution Strategies.International Conference on Artificial Immune Systems,ICARIS2003.Springer-Verlag,Berlin Heidelberg,2003::273-284P
[17]Du H F.,Jiao L C.,Gong M G.,Liu R C.Adaptive dynamic clone selection algorithms.International Conference on Rough Sets and Current Trends in Computing,RSCTC 2004,Springer-Verlag Berlin Heidelberg,2004:68-773P
[18]Cutello Vi.,Narzisi G.,Nicosia G.,Pavone M.An immunological algorithm for global numerical optimization.International Conference on Artificial Evolution,EA 2005,Springer-Verlag Berlin Heidelberg,2006:284-295P
[19]Meng H Y.,Zhang X H.,Liu S Y.A novel clonal selection for multi-modal function optimization.ICNC 2006,Part Ⅱ,Springer-Verlag Berlin Heidelberg,2006:63-72P
[20]位耀光,郑德玲,付冬梅等.基于生物免疫系统克隆选择机理和免疫 网络理论的免疫算法.北京科技大学学报.2005,27(2):245-249页
[21]Cutello V.,Nicosia G.,Pavone M.Exploring the capability of immune algorithms:a characterization of hypermutation operators.International Conference on Artificial Immune System,ICARIS 2004,Springer-Verlag Berlin Heidelberg,2004:263-276
[22]杜海峰,刘若辰,焦李成等.求解0-1背包问题的人工免疫抗体修正克隆算法.控制理论与应用.2005,22(3):348-352页
[23]杜海峰,焦李成,刘若辰.免疫优势克隆算法.电子与信息学报.2004,26(12):1919-1926页
[24]沈艳军,汪秉文.基于实数编码的克隆选择算法及其应用.华中科技大学学报(自然科学版).2004,32(2):41-42页
[25]Cruz-Cort'es N.,Trejo-Perez D.,Coello Coello C A.Handling constraints in global optimization using an artificial immune system.International Conference on Artificial Immune Systems,ICARIS 2005,Springer-Verlag,Berlin Heidelberg,2005,234-247P
[26]刘丽珏,蔡自兴,唐琏.采用粒群优化的免疫克隆算法.计算机应用.2006,26(4):886-888页
[27]Xie G.,Guo H B.,Xie K M.,Zhao W J.Research on clone mind evolution algorithm.RSFDGrC 2005.Springer-Verlag Berlin Heidelberg,2005:431-440P
[28]Ma W P.,Jiao L C.,Gong M G.,Shang R H.Immune clonal selection evolutionary strategy for constrained optimization.PRICAI 2006,Springer-Verlag,Berlin Heidelberg,2006:661-670P
[29]Du H F.,Gong M G.Jiao L C.,Liu R C.A novel algorithm of artificial immune system for high-dimensional function numerical optimization.Progress in natural science,2005,15(5):463-451P
[30]焦李成,杜海峰,刘芳等.免疫优化计算学习与识别.第一版.北京:科学出版社,2006
[31]刘芳,戚玉涛,公茂果.免疫克隆分类算法.电子学报.2005,33(12):2301-2307页
[32]张向荣,焦李成.基于免疫克隆选择算法的特征选择.复旦学报(自然科学版).2004,43(5):926-929页
[33]Mo,H.W.,Xu.L.F.Immune clone algorithm for attribute selection.The 8~(th) IASTED International Conference on Intelligent systems and control.Cambriage,USA,2005,297-302P
[34]丛琳,沙宇恒,焦李成.基于免疫克隆选择算法的图像分割.电子与信息学报.2006,28(7):1169-1173页
[35]Yang X H.,Jiao L C.,Qi Y T.,Jin H Y.Multifocus image fusion based on multiwavelet and immune clonal selection.The 2nd International Conference on Natural Computation,ICNC 2006,Part Ⅱ,Springer-Verlag,Berlin Heidelberg 2006:805 - 815P
[36]Liu F.,Zhao J.An immune clonal selection scheduling algorithm for input-queued switches.The Sixth International Conference on Simulated Evolution And Learning.International Conference on Simulated Evolution and Learning,SEAL 2006.Springer-Verlag,Berlin Heidelberg,2006:790-797P
[37]Mo H W.Xu L F.Immune clone algorithm for mining associate rules on dynamic database.The 17~(th) IEEE International Conference on tools with AI,ICTAI 05,Hongkong,2005:202-205P
[38]徐雪松,彭春华.动态环境下的免疫优化.计算机工程与应用.2007,43(1):27-29页
[39]罗印升,李人厚,张维玺.基于免疫机理的动态函数优化算法.西安交通大学学报.2005,39(4):384-388页
[40]张著洪,黄席樾.基于免疫应答原理的免疫算法及其在多模态函数优化中的应用.重庆大学学报.2003,26(9):130-134页
[41]张著洪,黄席樾.一种新的免疫算法及其在多模态函数优化中的应用.控制理论与应用.2004,21(1):17-21页
[42]李春华,毛宗源.基于人工免疫算法的多目标函数优化.计算机测量与控制.2005,13(3):278-280页
[43]罗印升,李人厚,张雷等.人工免疫算法在函数优化中的应用.西安交通大学学报.2003,37(8):840-843页
[44]罗文坚,张义国,王欣等.用于函数优化的进化非选择算法实验分析.哈尔滨工程大学学报.2006,27(增刊):158-163页
[45]徐雪松,诸静.多模态函数优化的免疫算法.浙江大学学报(工学版).2004,38(5):530-533页
[46]肖人彬,刘勇,窦刚.面向多峰值函数优化的人工免疫网络算法特性分析.模式识别与人工智能.2005,18(1):17-24页
[47]吕佳,熊忠阳.面向多模态函数优化的混沌免疫网络算法研究.计算机应用.2006,26(2):456-458页
[48]潘峰,丁云飞,汪为农.基于形状空间的克隆选择算法用于多峰值函数的优化.上海交通大学学报.2004,38(12):2089-2093页
[49]刘丽珏,蔡自兴.一种改进的克隆选择优化算法.计算机工程与应用.2006,13:30-32页
[50]靳宗信,刘光远,温万惠等.一种改进的用于多峰值函数优化.西南大学学报(自然科学版).2007,29(3):164-168页
[51]徐炜,贺占庄,黄士坦.一种新的免疫克隆选择算法在多峰寻优中的应用.武汉大学学报(工学版).2005,38(5):146-150页
[52]莫宏伟,金鸿章.用于函数优化的改进免疫克隆多样性算法.哈尔滨工程大学学报.2004,25(1):76-79页
[53]陈国良,王煦法等.遗传算法及其应用.北京:人民邮电出版社,1999
[54]焦李成等.免疫优化计算、学习与识别.北京:科学出版社,2007
[55]Bersini H.,Varela F.The immune recruitment mechanism:.,a selective evolutionary strategy.Proceedings of the 4th International Conference on Genetic Algorithms.Morgan Kaufman,1991:520-526P
[56]Bersini H,Varela F.The Immune Learning Mechanisms Reinforcement and Recruitment and Their Applications.Computing with Biological Metaphors.Chapman Hall,1994:166-192P
[57]Ishida Y,Active noise control by an immune algorithm:adaptation in immune system as an cvolution.Proc ICEC 96 Nagoya,1996:150- 153P
[58]谈英姿,沈炯,吕震中.免疫PID控制器在汽温控制系统中的应用研究.中国电机工程学报.2002,22(10):148-152页
[59]Ishiguro A.,Ichikaw A S.,Uchikaway A.A gait acquisition of a 6-legged robot using immune networks.IEEE RSJGI international conference on intelligent robots and systems.Munich,Germany,1994,2:1034-1041P
[60]Lee D W,Sim K B.Artificial immune network based cooperative control in collective autonomous mobile robots.6th IEEE International Workshop on Robot and Human Communication.Sendai,Japan,1997:58-63P
[61]Ootsuki J T.,Sekiguchi T.Application of the immune system network concept to sequential control.In Proc.IEEE SMC'99,1999:3869-874P
[62]Krishnakumar K.,Neidhoefer J.Immunized adaptive critics for level 2intelligent control.Proceedings of International Conference on Systems and Cybernetics(SMC).Orlando,USA,IEEE Press,1997:856-861P
[63]Takahashi K.,Yamada T.Application of an immune feedback mechanism to control systems.JSME International Journal,Series C,1998,41(2):184-191P
[64]Ding Y,Ren L.Fuzzy self-tuning immune feedback controller for tissue hyperthermia.FUZZ,IEEE 2000,The Ninth IEEE International Conference,San Antonio,2000,1::534-538P
[65]付冬梅,位耀光,郑德玲.基于双因子调节的免疫控制器的设计、实现与分析.信息与控制.2006,35(4):526-531页
[66]廖迎新,吴敏.基于免疫FNN算法的加热炉炉温优化控制.国防科技大学学报.2006,28(3):116-119页
[67]Kim D H.,Park J I.Intelligent PID control by immune algorithms based fuzzy rule auto-tuning.IFSA 2003,Springer-Verlag Berlin Heidelberg,2003:474-482P
[68]吴保林,裘丽华,王占林.基于人工免疫机制的马达调速控制研究.机床与液压.2006,4:160-162页
[69]王爽心,姜妍,李亚光.基于混合混沌优化策略的汽轮机调速系统模糊免疫P ID控制.中国电机工程学报.2006,26(11):70-74页
[70]Mark N.,Jan F.,Rosario R.,Wang X L.Don't touch me,I'm fine:robot autonomy using an artificial innate immune System.ICARIS 2006,Springer-Verlag,Berlin Heidelberg,2006:349-361P
[71]王瑞明,曾玉金,蒋静坪.基于免疫遗传算法的模糊神经网络在交流伺服系统中的应用.浙江大学学报.2005,39(8):1156-116页
[72]刘智勇,李水友.基于免疫遗传算法的区域交通自适应协调控制.控制理论与应用.2006,23(1):120-126页
[73]董梅,高康林.基于免疫算法PID控制器参数的优化设计.山东师范大学学报.2005,20(3):29-30页
[74]陈少白,谭光兴,毛宗源.基于一种人工免疫算法的PID参数优化.武汉科技大学学报.2006,29(3):313-315页
[75]王东风,李志清,李炎等.免疫优化变参数PID控制及其应用.华北电力大学学报.2004,31(1):36-40页
[76]刘屿,田联房,毛宗源.一种新型人工免疫算法的PID自整定研究.计 算机应用研究.2007,24(4):83-85页
[77]何宏,钱锋.基于免疫进化算法的PID参数整定.计算机应用.2007,27(5):1174-1176页
[78]刘金琨.先进PID控制MATLAB仿真.北京:电子工业出版社,2004
[79]Cheng R M.,Gen Y.A tutorial survey of job-shop scheduling problems using genetic algorithms.Part Ⅰ.International Journal of Computers and Industrial Engineering,1996,30(4):983-997P
[80]Adams J.,Balas E.,Zawack D.The shifting bottleneck procedure for job shop scheduling.International Journal of Flexible Manufacturing Systems.1987,34(3):391-401P
[81]Doublgeri Z.,D'alessandro G.,Magaletti N.A hierarchical knowledgebased scheduling and control for FMSs.Int.J.Computer integrated Manufacturing,1993,6(3):191-200P
[82]Wu S Y.David R A.An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing.Int.J.Prod.Res.1989,27(9):1603-1623P
[83]Jiang C Q.,Singh M G.,Hindi K S.Optimized routing in flexible manufacturing systems with blocking.IEEE Trans.on Systems,Man,and Cybernetics,1991,21(3):5889-595P
[84]Jeffcoat D E.,Robert L.B.Simulated annealing for resource-constrained scheduling.European Journal of Operational research,1993,70:43-51P
[85]Languna M.,Wesley B.,Fred G.Intelligent scheduling with tabu search:An application to jobs with linear delay penalties and sequence-dependent setup costs and times.Journal of Applied Intelligence,1993,3:159-172P
[86]Garcia B L.,Potvin J Y.,Rousseau J M.A parallel implementation of the tabu search heuristic for vehicle routing problems with time window constraints.Computers Ops.Res.,1994,21(9):1025-1033P
[87]Foo Y S.,Fuji Y.Integer linear programming neural networks for job-shop scheduling.IEEE Int.Conf.On NNS,San Diego,1988
[88]潘全科,王文宏,朱剑英.一类解决Job Shop问题的改进遗传算法.中国机械工程.2006,8(17):866-869页
[89]朱双冬,夏文明.基于神经网络的Job-Shop类调度问题.机电工程.2007,24(1):64-65页
[90]余建军,孙树栋,郑锋.基于动态评价免疫算法的车间作业调度研究.机械工程学报.2005,41(3):25-30页
[91]Vassilis S K.,Yannis A P.An exact discrete-event model and control policies for production lines with buffers.IEEE Trans.On automatic control,1991,36(5):222-234P
[92]Brandimarte P.Routing and scheduling in a flexible job shop by taboo search.Annals of Operations Research,1993,41(22):157-183P
[93]Zribi N,Kacem Ⅰ,Kamel A,Borne P.Optimization by phase for the flexible job-shop scheduling problem.The 5th Asian Control Conference,2004:1889-1899P
[94]Glover F.Future paths for integer programming and links to artificial intelligence.Computer Oper.Res.,1986,13(5):533-549P
[95]Widmer M.,Hertz A.A new heuristic method for the flow shop sequencing problem.European Journal of Oper.Res.,1989,41(2):186-193P
[96]Glover F.New approaches for heuristic search:a bilateral linkage with artificial intelligence.European Journal of Oper.Res.,1989,39,119-130P
[97]王自强,冯博琴.车间流程的免疫调度算法.西安交通大学学报.2004,38(10):1031-1034页
[98]周亚勤,李蓓智,杨建国.调整时间与工序顺序相关的Job Shop调度 优化方法研究.中国机械工程.2005,16(10):914-918页
[99]杜广宇,王莉.一种改进的人工免疫算法在模糊Flow-shop调度问题上的应用.鞍山科技大学学报.2005,28(6):432-435页
[100]苏生,战德臣,徐晓飞.基于免疫算法的并行机间歇过程模糊生产调度.计算机集成制造系统.2006,12(8):1252-1257页
[101]李蓓智,杨建国,丁惠敏.基于生物免疫机理的智能调度系统建模与仿真.计算机集成制造系统.2002,8(6):446-450页
[102]杨建国,丁慧敏,李蓓智.解决多目标Flow-shop问题的生物免疫调度算法.机械设计与研究.2002,18(4):28-30页
[103]陈廷伟,张斌,郝宪文.基于免疫遗传算法的网格任务调度东北大学学报.2007,28(3):329-332页
[104]马彦,莫蓉,常智勇.基于免疫机制解决JSP调度问题算法研究机床与液压.2007,35(5):39-41页
[105]李季,文俊浩,张伟等.基于人工免疫的网格资源任务调度算法计算机科学.2004,31(9):55-57页
[106]Giffler B.Thompson G.Algorithms for solving production scheduling problems.Operations Research,1960,8(4):487-503P
[107]Baker K.Scudder G.Sequencing with earliness and tardiness penalties:A review.Operations Research,1990,38:22-36P
[108]史忠植.知识发现.北京:清华大学出版社,2002
[109]Hunt J E.,Cooke D E.Learning using an artificial immune system.In Journal of Network and Computer Applications:Special Issue on Intelligent Systems:Design and Application.1996,19:189-212P
[110]Timmis J.,Neal M.,Hunt J.An artificial immune system for data analysis.BioSystems,2000,55:143-150P
[111]Timmis J.,Neal M.A resource limited artificial immune system for data analysis.Knowledge-Based Systems,2001,14:121-130P
[112] Knight T. , Timmis J. A multi-layered immune inspired approach to data mining. Proceedings of the 4th International Conference on Recent Advances in Soft Computing, Nottingham, UK, 2002: 266-271P
[113] Wierzchon S T. , Kuzelewska U. Stable cluster formations in an artificial immune system. 1 st International Conference on Artificial Immune Systems, University of Kent at Canterbury, UK, University of Kent, 2002:68-75P
[114] Sotiropoulos D N. , Tsihrintzis G A. , Savvopoulos A. , Virvou M. Artificial immune system-based customer data clustering in an e-shopping application . Tenth International Conference on Based Intelligent Information & Engineering Systems, KES 2006, Part I,Springer-Verlag Berlin Heidelberg, 2006: 960-967P
[115] Neal M . Meta-stable memory in an artificial immune network. Proceedings of the Second International Conference on Artificial Immune Systems, 2003: 168-180P
[116] Hart E. , Ross P. Exploiting the analogy between immunology and sparse distributed memories : a system for clustering non-stationary data. Proceedings of 1st International Conference on Artificial Immune Systems (ICARIS-2002), University of Kent at Canterbury, UK, September, 2002:: 9-11P
[117] Bentley P J. , Timmis J. A Fractal Immune Network. Proceedings of the Third International Conference on Artificial Immune Systems (ICARIS-2004), Catania, Italy. 2004: 13-16P
[118] Nasaroui O. , Gonzalez F. , DasguptaD. The Fuzzy artificial immune system: motivations, basic concepts, and application to clustering and web profiling. IEEE International Conference on Fuzzy Systems. Proceedings of the IEEE World Congress on Computational Intelligence, Hawaii, 2002
[119]Nasaroui O.,González F.,Cardona C.,Rojas C.Dasgupta D.A scalable artificial immune system model for dynamic unsupervised Learning.Proceedings of the Genetic and Evolutionary Computation Conference(GECCO),2003
[120]De Castro L N.,Von Zuben F J.AiNet:An artificial immune network for data analysis.Data Mining:A Heuristic Approach,Idea Group Publishing,USA,2001
[121]George B B.,de Castro L N.,Von Zuben F J.A hierarchical immune network applied to gene expression data.International Conference on Artificial Immune Systems,ICARIS 2004,Springer-Verlag,Berlin Heidelberg,2004:14-27P
[122]Coelho G P.,Von Zuben F J.Omni-aiNet:An immune-inspired approach for omni optimization.International Conference on Artificial Immune Systems,ICARIS 2006.2006:294-308P
[123]Wierzchon S,Kuzelewska U.Stable clusters formation in an artificial immune system.1~(st) International Conference on Artificial Immune Systems (ICARIS),University of Kent at Canterbury,UK,September 9th-11th,2002
[124]Bezerra G B.,de Castro L N.Bioinformatics data analysis using an artificial immune network.Lecture Notes in Computer Sciences,Proc.of Second International Conference on Artificial Immune Systems,ICARIS 2003.Edinburgh,UK,2003:22-33P
[125]莫宏伟,吕淑萍,管凤旭等.基于人工免疫系统的数据挖掘技术原理与应用研究.计算机工程与应用.2004,40(14):28-33页
[126]Ciesielski K.,Wierzchon S.Klopotek M.An Immune Network for Contextual Text Data Clustering.International Conference on Artificial Immune Systems,ICARIS 2006,Springer-Verlag Berlin Heidelberg, 2006:432-445P
[127]Tang N.,Vemuri R.An artificial immune system approach to document clustering.20th ACM Symposium on Applied Computing.Santa Fe,New Mexico,USA,2005
[128]Xu L F.,Mo H W.,Wang K J.Document clustering based on modified artificial immune network,First International Conference on Rough Sets and Knowledge Technology,Springer-Verlag,Berlin Heidelberg,2006
[129]Christoph F E.,Nidal Z.,Zhao Z.Supervised Clustering -Algorithms and Benefits.Technical Report Number UH-CS-05-10,2005
[2] Farmer J D., Packard N H. ,Perelson A. S. The immune system, adaptation,and machine learning, Physica, 1986, 22D, 187-204P
[3] Varela F J, Stewart J. Dynamics of a class of immune network. Global Stability of Idiotype Interactions. J. Theoretical Biology, 1990, 144(1):93-101P
[4] Perelson A S. Immune network theory. Immunological review 1989, 10:5-36P
[5] Bersini H. , Varela FJ. Hints for Adaptive Problem Solving Gleaned from Immune Networks. Proc of the First Workshop on Parallel Problem Solving from Nature, 1990:343-354P
[6] Ishida Y. Fully distributed diagnosis by PDP learning algorithm: towards immune network PDP model. Proc. of IJCNN 90, San Diego, 1990
[7] Forrest S. , Perelson A. , Allen L. , Cherukuri R. Self-nonself discrimination in a computer. Proc. of the IEEE Symposium on Research in Security and Privacy, 1994: 202-212P
[8] Cooke D E. , Hunt J E. Recognizing promoter sequences using an artificial immune system. Proc. Intelligent Systems in Molecular Biology (ISMB'95),Pub AAAI Press, 1995: 89-97P
[9] Hunt J E., Cooke D E. Learning using an artificial immune system. Journal of Network and Computer Applications. 1996, 19: 189 -212P
[10] Burnet F M. The clonal selection theory of acquired immunity. Cambridge University Press, 1959
[11] De Castro L N. , Von Zuben F J. Artificial immune systems:: part I - basic theory and applications.Technical Report- RT DCA 01/99,89,1999
[12]Dasgupta D.,Forrest S.Artificial immune systems in industrial applications.Proc.of the IPMM'99,1999
[13]Timmis J.,Neal M.,Hunt J.An artificial immune system for data analysis.Biosystems,2000,55(1/3):143-150P
[14]De Castro L N.,Timmis J.Artificial immune systems:a new computational intelligence approach.Springer-verlag,Berlin Heidelberg,2002
[15]De Castro L N.,Von Zuben F J.Learning and optimization using the clonal selection principle.IEEE Transactions on Evolutionary Computation.2002,6(3):239-251P
[16]Joanne H.,Walker Simon M G.Dynamic function optimization::comparing the performance of clonal selection and evolution Strategies.International Conference on Artificial Immune Systems,ICARIS2003.Springer-Verlag,Berlin Heidelberg,2003::273-284P
[17]Du H F.,Jiao L C.,Gong M G.,Liu R C.Adaptive dynamic clone selection algorithms.International Conference on Rough Sets and Current Trends in Computing,RSCTC 2004,Springer-Verlag Berlin Heidelberg,2004:68-773P
[18]Cutello Vi.,Narzisi G.,Nicosia G.,Pavone M.An immunological algorithm for global numerical optimization.International Conference on Artificial Evolution,EA 2005,Springer-Verlag Berlin Heidelberg,2006:284-295P
[19]Meng H Y.,Zhang X H.,Liu S Y.A novel clonal selection for multi-modal function optimization.ICNC 2006,Part Ⅱ,Springer-Verlag Berlin Heidelberg,2006:63-72P
[20]位耀光,郑德玲,付冬梅等.基于生物免疫系统克隆选择机理和免疫 网络理论的免疫算法.北京科技大学学报.2005,27(2):245-249页
[21]Cutello V.,Nicosia G.,Pavone M.Exploring the capability of immune algorithms:a characterization of hypermutation operators.International Conference on Artificial Immune System,ICARIS 2004,Springer-Verlag Berlin Heidelberg,2004:263-276
[22]杜海峰,刘若辰,焦李成等.求解0-1背包问题的人工免疫抗体修正克隆算法.控制理论与应用.2005,22(3):348-352页
[23]杜海峰,焦李成,刘若辰.免疫优势克隆算法.电子与信息学报.2004,26(12):1919-1926页
[24]沈艳军,汪秉文.基于实数编码的克隆选择算法及其应用.华中科技大学学报(自然科学版).2004,32(2):41-42页
[25]Cruz-Cort'es N.,Trejo-Perez D.,Coello Coello C A.Handling constraints in global optimization using an artificial immune system.International Conference on Artificial Immune Systems,ICARIS 2005,Springer-Verlag,Berlin Heidelberg,2005,234-247P
[26]刘丽珏,蔡自兴,唐琏.采用粒群优化的免疫克隆算法.计算机应用.2006,26(4):886-888页
[27]Xie G.,Guo H B.,Xie K M.,Zhao W J.Research on clone mind evolution algorithm.RSFDGrC 2005.Springer-Verlag Berlin Heidelberg,2005:431-440P
[28]Ma W P.,Jiao L C.,Gong M G.,Shang R H.Immune clonal selection evolutionary strategy for constrained optimization.PRICAI 2006,Springer-Verlag,Berlin Heidelberg,2006:661-670P
[29]Du H F.,Gong M G.Jiao L C.,Liu R C.A novel algorithm of artificial immune system for high-dimensional function numerical optimization.Progress in natural science,2005,15(5):463-451P
[30]焦李成,杜海峰,刘芳等.免疫优化计算学习与识别.第一版.北京:科学出版社,2006
[31]刘芳,戚玉涛,公茂果.免疫克隆分类算法.电子学报.2005,33(12):2301-2307页
[32]张向荣,焦李成.基于免疫克隆选择算法的特征选择.复旦学报(自然科学版).2004,43(5):926-929页
[33]Mo,H.W.,Xu.L.F.Immune clone algorithm for attribute selection.The 8~(th) IASTED International Conference on Intelligent systems and control.Cambriage,USA,2005,297-302P
[34]丛琳,沙宇恒,焦李成.基于免疫克隆选择算法的图像分割.电子与信息学报.2006,28(7):1169-1173页
[35]Yang X H.,Jiao L C.,Qi Y T.,Jin H Y.Multifocus image fusion based on multiwavelet and immune clonal selection.The 2nd International Conference on Natural Computation,ICNC 2006,Part Ⅱ,Springer-Verlag,Berlin Heidelberg 2006:805 - 815P
[36]Liu F.,Zhao J.An immune clonal selection scheduling algorithm for input-queued switches.The Sixth International Conference on Simulated Evolution And Learning.International Conference on Simulated Evolution and Learning,SEAL 2006.Springer-Verlag,Berlin Heidelberg,2006:790-797P
[37]Mo H W.Xu L F.Immune clone algorithm for mining associate rules on dynamic database.The 17~(th) IEEE International Conference on tools with AI,ICTAI 05,Hongkong,2005:202-205P
[38]徐雪松,彭春华.动态环境下的免疫优化.计算机工程与应用.2007,43(1):27-29页
[39]罗印升,李人厚,张维玺.基于免疫机理的动态函数优化算法.西安交通大学学报.2005,39(4):384-388页
[40]张著洪,黄席樾.基于免疫应答原理的免疫算法及其在多模态函数优化中的应用.重庆大学学报.2003,26(9):130-134页
[41]张著洪,黄席樾.一种新的免疫算法及其在多模态函数优化中的应用.控制理论与应用.2004,21(1):17-21页
[42]李春华,毛宗源.基于人工免疫算法的多目标函数优化.计算机测量与控制.2005,13(3):278-280页
[43]罗印升,李人厚,张雷等.人工免疫算法在函数优化中的应用.西安交通大学学报.2003,37(8):840-843页
[44]罗文坚,张义国,王欣等.用于函数优化的进化非选择算法实验分析.哈尔滨工程大学学报.2006,27(增刊):158-163页
[45]徐雪松,诸静.多模态函数优化的免疫算法.浙江大学学报(工学版).2004,38(5):530-533页
[46]肖人彬,刘勇,窦刚.面向多峰值函数优化的人工免疫网络算法特性分析.模式识别与人工智能.2005,18(1):17-24页
[47]吕佳,熊忠阳.面向多模态函数优化的混沌免疫网络算法研究.计算机应用.2006,26(2):456-458页
[48]潘峰,丁云飞,汪为农.基于形状空间的克隆选择算法用于多峰值函数的优化.上海交通大学学报.2004,38(12):2089-2093页
[49]刘丽珏,蔡自兴.一种改进的克隆选择优化算法.计算机工程与应用.2006,13:30-32页
[50]靳宗信,刘光远,温万惠等.一种改进的用于多峰值函数优化.西南大学学报(自然科学版).2007,29(3):164-168页
[51]徐炜,贺占庄,黄士坦.一种新的免疫克隆选择算法在多峰寻优中的应用.武汉大学学报(工学版).2005,38(5):146-150页
[52]莫宏伟,金鸿章.用于函数优化的改进免疫克隆多样性算法.哈尔滨工程大学学报.2004,25(1):76-79页
[53]陈国良,王煦法等.遗传算法及其应用.北京:人民邮电出版社,1999
[54]焦李成等.免疫优化计算、学习与识别.北京:科学出版社,2007
[55]Bersini H.,Varela F.The immune recruitment mechanism:.,a selective evolutionary strategy.Proceedings of the 4th International Conference on Genetic Algorithms.Morgan Kaufman,1991:520-526P
[56]Bersini H,Varela F.The Immune Learning Mechanisms Reinforcement and Recruitment and Their Applications.Computing with Biological Metaphors.Chapman Hall,1994:166-192P
[57]Ishida Y,Active noise control by an immune algorithm:adaptation in immune system as an cvolution.Proc ICEC 96 Nagoya,1996:150- 153P
[58]谈英姿,沈炯,吕震中.免疫PID控制器在汽温控制系统中的应用研究.中国电机工程学报.2002,22(10):148-152页
[59]Ishiguro A.,Ichikaw A S.,Uchikaway A.A gait acquisition of a 6-legged robot using immune networks.IEEE RSJGI international conference on intelligent robots and systems.Munich,Germany,1994,2:1034-1041P
[60]Lee D W,Sim K B.Artificial immune network based cooperative control in collective autonomous mobile robots.6th IEEE International Workshop on Robot and Human Communication.Sendai,Japan,1997:58-63P
[61]Ootsuki J T.,Sekiguchi T.Application of the immune system network concept to sequential control.In Proc.IEEE SMC'99,1999:3869-874P
[62]Krishnakumar K.,Neidhoefer J.Immunized adaptive critics for level 2intelligent control.Proceedings of International Conference on Systems and Cybernetics(SMC).Orlando,USA,IEEE Press,1997:856-861P
[63]Takahashi K.,Yamada T.Application of an immune feedback mechanism to control systems.JSME International Journal,Series C,1998,41(2):184-191P
[64]Ding Y,Ren L.Fuzzy self-tuning immune feedback controller for tissue hyperthermia.FUZZ,IEEE 2000,The Ninth IEEE International Conference,San Antonio,2000,1::534-538P
[65]付冬梅,位耀光,郑德玲.基于双因子调节的免疫控制器的设计、实现与分析.信息与控制.2006,35(4):526-531页
[66]廖迎新,吴敏.基于免疫FNN算法的加热炉炉温优化控制.国防科技大学学报.2006,28(3):116-119页
[67]Kim D H.,Park J I.Intelligent PID control by immune algorithms based fuzzy rule auto-tuning.IFSA 2003,Springer-Verlag Berlin Heidelberg,2003:474-482P
[68]吴保林,裘丽华,王占林.基于人工免疫机制的马达调速控制研究.机床与液压.2006,4:160-162页
[69]王爽心,姜妍,李亚光.基于混合混沌优化策略的汽轮机调速系统模糊免疫P ID控制.中国电机工程学报.2006,26(11):70-74页
[70]Mark N.,Jan F.,Rosario R.,Wang X L.Don't touch me,I'm fine:robot autonomy using an artificial innate immune System.ICARIS 2006,Springer-Verlag,Berlin Heidelberg,2006:349-361P
[71]王瑞明,曾玉金,蒋静坪.基于免疫遗传算法的模糊神经网络在交流伺服系统中的应用.浙江大学学报.2005,39(8):1156-116页
[72]刘智勇,李水友.基于免疫遗传算法的区域交通自适应协调控制.控制理论与应用.2006,23(1):120-126页
[73]董梅,高康林.基于免疫算法PID控制器参数的优化设计.山东师范大学学报.2005,20(3):29-30页
[74]陈少白,谭光兴,毛宗源.基于一种人工免疫算法的PID参数优化.武汉科技大学学报.2006,29(3):313-315页
[75]王东风,李志清,李炎等.免疫优化变参数PID控制及其应用.华北电力大学学报.2004,31(1):36-40页
[76]刘屿,田联房,毛宗源.一种新型人工免疫算法的PID自整定研究.计 算机应用研究.2007,24(4):83-85页
[77]何宏,钱锋.基于免疫进化算法的PID参数整定.计算机应用.2007,27(5):1174-1176页
[78]刘金琨.先进PID控制MATLAB仿真.北京:电子工业出版社,2004
[79]Cheng R M.,Gen Y.A tutorial survey of job-shop scheduling problems using genetic algorithms.Part Ⅰ.International Journal of Computers and Industrial Engineering,1996,30(4):983-997P
[80]Adams J.,Balas E.,Zawack D.The shifting bottleneck procedure for job shop scheduling.International Journal of Flexible Manufacturing Systems.1987,34(3):391-401P
[81]Doublgeri Z.,D'alessandro G.,Magaletti N.A hierarchical knowledgebased scheduling and control for FMSs.Int.J.Computer integrated Manufacturing,1993,6(3):191-200P
[82]Wu S Y.David R A.An application of discrete-event simulation to on-line control and scheduling in flexible manufacturing.Int.J.Prod.Res.1989,27(9):1603-1623P
[83]Jiang C Q.,Singh M G.,Hindi K S.Optimized routing in flexible manufacturing systems with blocking.IEEE Trans.on Systems,Man,and Cybernetics,1991,21(3):5889-595P
[84]Jeffcoat D E.,Robert L.B.Simulated annealing for resource-constrained scheduling.European Journal of Operational research,1993,70:43-51P
[85]Languna M.,Wesley B.,Fred G.Intelligent scheduling with tabu search:An application to jobs with linear delay penalties and sequence-dependent setup costs and times.Journal of Applied Intelligence,1993,3:159-172P
[86]Garcia B L.,Potvin J Y.,Rousseau J M.A parallel implementation of the tabu search heuristic for vehicle routing problems with time window constraints.Computers Ops.Res.,1994,21(9):1025-1033P
[87]Foo Y S.,Fuji Y.Integer linear programming neural networks for job-shop scheduling.IEEE Int.Conf.On NNS,San Diego,1988
[88]潘全科,王文宏,朱剑英.一类解决Job Shop问题的改进遗传算法.中国机械工程.2006,8(17):866-869页
[89]朱双冬,夏文明.基于神经网络的Job-Shop类调度问题.机电工程.2007,24(1):64-65页
[90]余建军,孙树栋,郑锋.基于动态评价免疫算法的车间作业调度研究.机械工程学报.2005,41(3):25-30页
[91]Vassilis S K.,Yannis A P.An exact discrete-event model and control policies for production lines with buffers.IEEE Trans.On automatic control,1991,36(5):222-234P
[92]Brandimarte P.Routing and scheduling in a flexible job shop by taboo search.Annals of Operations Research,1993,41(22):157-183P
[93]Zribi N,Kacem Ⅰ,Kamel A,Borne P.Optimization by phase for the flexible job-shop scheduling problem.The 5th Asian Control Conference,2004:1889-1899P
[94]Glover F.Future paths for integer programming and links to artificial intelligence.Computer Oper.Res.,1986,13(5):533-549P
[95]Widmer M.,Hertz A.A new heuristic method for the flow shop sequencing problem.European Journal of Oper.Res.,1989,41(2):186-193P
[96]Glover F.New approaches for heuristic search:a bilateral linkage with artificial intelligence.European Journal of Oper.Res.,1989,39,119-130P
[97]王自强,冯博琴.车间流程的免疫调度算法.西安交通大学学报.2004,38(10):1031-1034页
[98]周亚勤,李蓓智,杨建国.调整时间与工序顺序相关的Job Shop调度 优化方法研究.中国机械工程.2005,16(10):914-918页
[99]杜广宇,王莉.一种改进的人工免疫算法在模糊Flow-shop调度问题上的应用.鞍山科技大学学报.2005,28(6):432-435页
[100]苏生,战德臣,徐晓飞.基于免疫算法的并行机间歇过程模糊生产调度.计算机集成制造系统.2006,12(8):1252-1257页
[101]李蓓智,杨建国,丁惠敏.基于生物免疫机理的智能调度系统建模与仿真.计算机集成制造系统.2002,8(6):446-450页
[102]杨建国,丁慧敏,李蓓智.解决多目标Flow-shop问题的生物免疫调度算法.机械设计与研究.2002,18(4):28-30页
[103]陈廷伟,张斌,郝宪文.基于免疫遗传算法的网格任务调度东北大学学报.2007,28(3):329-332页
[104]马彦,莫蓉,常智勇.基于免疫机制解决JSP调度问题算法研究机床与液压.2007,35(5):39-41页
[105]李季,文俊浩,张伟等.基于人工免疫的网格资源任务调度算法计算机科学.2004,31(9):55-57页
[106]Giffler B.Thompson G.Algorithms for solving production scheduling problems.Operations Research,1960,8(4):487-503P
[107]Baker K.Scudder G.Sequencing with earliness and tardiness penalties:A review.Operations Research,1990,38:22-36P
[108]史忠植.知识发现.北京:清华大学出版社,2002
[109]Hunt J E.,Cooke D E.Learning using an artificial immune system.In Journal of Network and Computer Applications:Special Issue on Intelligent Systems:Design and Application.1996,19:189-212P
[110]Timmis J.,Neal M.,Hunt J.An artificial immune system for data analysis.BioSystems,2000,55:143-150P
[111]Timmis J.,Neal M.A resource limited artificial immune system for data analysis.Knowledge-Based Systems,2001,14:121-130P
[112] Knight T. , Timmis J. A multi-layered immune inspired approach to data mining. Proceedings of the 4th International Conference on Recent Advances in Soft Computing, Nottingham, UK, 2002: 266-271P
[113] Wierzchon S T. , Kuzelewska U. Stable cluster formations in an artificial immune system. 1 st International Conference on Artificial Immune Systems, University of Kent at Canterbury, UK, University of Kent, 2002:68-75P
[114] Sotiropoulos D N. , Tsihrintzis G A. , Savvopoulos A. , Virvou M. Artificial immune system-based customer data clustering in an e-shopping application . Tenth International Conference on Based Intelligent Information & Engineering Systems, KES 2006, Part I,Springer-Verlag Berlin Heidelberg, 2006: 960-967P
[115] Neal M . Meta-stable memory in an artificial immune network. Proceedings of the Second International Conference on Artificial Immune Systems, 2003: 168-180P
[116] Hart E. , Ross P. Exploiting the analogy between immunology and sparse distributed memories : a system for clustering non-stationary data. Proceedings of 1st International Conference on Artificial Immune Systems (ICARIS-2002), University of Kent at Canterbury, UK, September, 2002:: 9-11P
[117] Bentley P J. , Timmis J. A Fractal Immune Network. Proceedings of the Third International Conference on Artificial Immune Systems (ICARIS-2004), Catania, Italy. 2004: 13-16P
[118] Nasaroui O. , Gonzalez F. , DasguptaD. The Fuzzy artificial immune system: motivations, basic concepts, and application to clustering and web profiling. IEEE International Conference on Fuzzy Systems. Proceedings of the IEEE World Congress on Computational Intelligence, Hawaii, 2002
[119]Nasaroui O.,González F.,Cardona C.,Rojas C.Dasgupta D.A scalable artificial immune system model for dynamic unsupervised Learning.Proceedings of the Genetic and Evolutionary Computation Conference(GECCO),2003
[120]De Castro L N.,Von Zuben F J.AiNet:An artificial immune network for data analysis.Data Mining:A Heuristic Approach,Idea Group Publishing,USA,2001
[121]George B B.,de Castro L N.,Von Zuben F J.A hierarchical immune network applied to gene expression data.International Conference on Artificial Immune Systems,ICARIS 2004,Springer-Verlag,Berlin Heidelberg,2004:14-27P
[122]Coelho G P.,Von Zuben F J.Omni-aiNet:An immune-inspired approach for omni optimization.International Conference on Artificial Immune Systems,ICARIS 2006.2006:294-308P
[123]Wierzchon S,Kuzelewska U.Stable clusters formation in an artificial immune system.1~(st) International Conference on Artificial Immune Systems (ICARIS),University of Kent at Canterbury,UK,September 9th-11th,2002
[124]Bezerra G B.,de Castro L N.Bioinformatics data analysis using an artificial immune network.Lecture Notes in Computer Sciences,Proc.of Second International Conference on Artificial Immune Systems,ICARIS 2003.Edinburgh,UK,2003:22-33P
[125]莫宏伟,吕淑萍,管凤旭等.基于人工免疫系统的数据挖掘技术原理与应用研究.计算机工程与应用.2004,40(14):28-33页
[126]Ciesielski K.,Wierzchon S.Klopotek M.An Immune Network for Contextual Text Data Clustering.International Conference on Artificial Immune Systems,ICARIS 2006,Springer-Verlag Berlin Heidelberg, 2006:432-445P
[127]Tang N.,Vemuri R.An artificial immune system approach to document clustering.20th ACM Symposium on Applied Computing.Santa Fe,New Mexico,USA,2005
[128]Xu L F.,Mo H W.,Wang K J.Document clustering based on modified artificial immune network,First International Conference on Rough Sets and Knowledge Technology,Springer-Verlag,Berlin Heidelberg,2006
[129]Christoph F E.,Nidal Z.,Zhao Z.Supervised Clustering -Algorithms and Benefits.Technical Report Number UH-CS-05-10,2005