生物启发计算若干关键技术与应用研究
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摘要
生物启发计算是在生物界自然现象的启示下获得灵感,研究开发智能计算模型和算法的新兴学科,包含遗传算法、粒子群算法、人工免疫算法、蚁群算法、神经网络等算法。生物启发计算作为高效的优化算法广泛应用于数据挖掘、机器人应用和网络入侵检测等领域;也为复杂问题的求解提供了新的解决方法。与成熟学科相比,生物启发计算的研究仍处于初步探索阶段。为提高生物启发计算的应用效率,本文研究生物启发计算的三种典型计算方法:遗传算法、粒子群算法和人工免疫算法,分别提出协作协进化遗传算法、基于惩罚机制的自适应交叉粒子群算法和基于多种群遗传算法的抗体生成算法等,并利用多机器人路径规划和入侵检测系统验证提出算法的有效性,为解决生物启发计算的“早熟”问题、局部收敛问题、降低计算复杂度等关键问题提供了新的思路和方法。
本文主要工作包括:
1.针对遗传算法求解多目标优化问题中存在的早熟问题,设计了一种染色体长度可变、混合编码的Messy遗传算法(Messy GA),并在此基础上提出全局适应度函数,实现了基于协作协进化的Messy GA(CCMGA)。在传统遗传算法的选择操作、交叉操作和变异操作基础上,利用简化操作、平滑操作和修复操作来辅助目标函数的优化。针对遗传算法容易丧失种群多样性的问题,结合混沌机制提高CCMGA的局部搜索能力。最后利用CCMGA实现多机器人路径规划,通过Matlab的仿真实验模拟多机器人在相对复杂的地图环境下完成动态路径规划,验证算法有效克服早熟问题,并且CCMGA能提高遗传算法的收敛速度和最优解。
2.粒子群算法近年出现了多种改进的方案,但均存在易陷入局部收敛的问题。本文提出一种基于惩罚机制的自适应交叉粒子群算法,有效克服局部收敛,并利用参数自适应解决单峰和多峰约束优化问题。根据粒子群进化过程中种群多样性模型,引入交叉操作,利用柯西不等式证明交叉粒子群算法通过保持种群多样性克服早熟和局部收敛,从而得到全局最优解。建立有限状态组成的马尔科夫链模型描述粒子群算法进化状态转换过程,有效控制粒子群算法收敛到全局最优,形成了自适应交叉粒子群算法。基于改进H策略和简化P策略惩罚机制,优化典型的Benchmark函数,分析实验结果得到:根据问题本身单峰和多峰的不同特性,参数设置影响收敛速度和最优解,因此本文提出参数自适应计算公式,有效提高粒子群算法求解单峰和多峰优化问题的性能。
3.针对人工免疫算法中抗体抗原最优阈值的求解困难,本文提出了匹配阈值预测模型,分析抗体抗原匹配规律,利用预测模型计算获得最优阈值,提高抗体检测效率。针对抗体生成算法复杂度高、生成抗体检测率低和抗体集合庞大的问题,提出了基于多种群遗传算法的抗体生成算法(MPTMA)。在形态学空间利用覆盖原理分析抗体抗原匹配,有效降低抗体集合的冗余度,减小抗体规模,保持抗体的多样性,提高抗体检测率。从理论和仿真分别证明MPTMA提高了抗体检测率、降低了抗体生成算法的时间复杂度。
4.将提出的基于阈值预测模型的MPTMA应用于入侵检测系统,提出了信息预处理机制。利用最小信息熵离散化算法对网络数据进行离散化处理,并结合PCA特征提取算法对数据进行特征提取。结合基于否定选择算法的快速匹配检测器和基于克隆选择算法的智能进化检测器,利用基于克隆选择算法的智能进化自学习得到的入侵特征更新前者的特征库,实现快速匹配检测器和基于克隆选择算法的智能进化检测器的协作,保证了混合检测器的检测实时性和准确性。仿真实验证明基于预测模型的MPTMA生成检测器提高了检测率,与传统的方法相比,在优化结果、收敛速度和稳定性上均有明显提高;同时相对单独使用上述两种检测器,混合检测系统在实时性、检测率和误测率等方面具备更好的性能。
本文的主要创新点:
1.针对遗传算法求解多目标优化存在早熟的问题,提出了基于协作协进化机制的Messy GA,构建全局适应度函数,利用辅助算子优化,并结合混沌机制提高局部搜索能力。
2.针对粒子群算法易陷入局部收敛问题,提出基于惩罚机制的交叉粒子群算法,分析种群收敛规律提出自适应交叉概率模型,求解单峰和多峰优化问题实现参数自适应,有效克服局部收敛,提高优化性能。
3.提出匹配阈值的预测模型,克服最优阈值的求解困难,在此基础上提出基于多种群遗传算法的抗体生成算法,在形态学空间利用覆盖原理分析抗体抗原匹配,MPTMA提高抗体检测率、降低抗体生成算法的时间复杂度。
4.将MPTMA作为入侵检测系统的检测器生成算法,利用最小信息熵离散化算法和PCA特征提取算法预处理信息,提出了结合基于否定选择算法的快速匹配检测器和基于克隆选择算法的智能进化检测器的混合检测器,在优化结果、收敛速度和稳定性上提高性能。
Bio-inspired computing, enlightened by natural intelligence of biological world, is a novel science for research and development of intelligent computing models and algorithms. Bio-inspired computing, including genetic algorithm, particle swarm optimization, artificial immune algorithm, ant clonal algorithm, neural network algorithm and etc., is considered as efficient optimizing algorithms widely applied in areas as artificial intelligence, machine learning, data mining, robots, network intrusion detection and etc. It provides novel solutions for complex problems. Compared with matured sciences, bio-inspired computing is still young which needs further discuss and research. In order to improve the efficiency, this paper is focused on three classic Bio-inspired computing algorithms: genetic algorithm, particle swarm optimization and artificial immune computing. CCMGA, penalty mechanism based crossover PSO and MPTMA are brought up, and the put forward algorithms are applied to multiple robots path planning and intrusion detection system to testify their efficiency. This paper provides novel ideas and methods for solving premature, local convergence and algorithm complexity problems in Bio-inspired computing.
The main work of this thesis can be concluded as:
1. Aiming at premature problem of GA optimizing multiple objective problems, drawbacks of traditional coding and fitness function definition in GA are pointed out. Messy GA with variable length of chromosome and hybrid coding is brought up, based on which a global fitness function is defined to implement cooperative co-evolution Messy GA (CCMGA). Besides operations of selection, crossover and mutation in traditional GA, simplify, smooth and repair operators are adopted to assist optimizing objective functions. Aiming at population diversity lost in GA, chaotic mechanism is applied to improve local search ability of CCMGA. Finally CCMGA is applied for multiple robots path planning. Matlab simulation results testify that multiple robots are able to optimize paths in various complicated maps. CCMGA is proved be capable of overcoming premature problem in GA, on basis of which convergence speed and optimized results are improved.
2. Particle swarm optimization (PSO) has obvious shortcoming: local convergence. In this paper, penalty mechanism based self-adaptive crossover PSO is put forward to overcome local convergence problem Based on population diversity model in evolutionary process for particles, crossover operation applied into PSO proved by Cauchy inequality is used to maintain population diversity to overcome problem of premature and local convergence and achieve global optimum solution. Evolutionary state transition process is depicted by Markov model consisted with finite-states, and self-adaptive crossover PSO is implemented. Penalty mechanism based Self-adaptive crossover PSO is designed for solving constrained optimization problems. Based on improved H strategy and simplified P strategy, experiments on benchmark functions demonstrate that parameters affect performance when optimizing unimodal and multimodal problems. A general calculating formula is put forward to control parameters for optimizing unimodal and multi-modal function optimizations respectively, which overcomes the in prior parameter setting difficulty.
3. Aiming at optimal matching threshold of antibody generation, a matching threshold prediction model is brought up in this paper. Analyzing antigen and antibody matching principle, optimized threshold is calculated by prediction model. In order to decrease complexity of antibody maturation algorithm, improve detection rate and smaller antibody set, multiple population GA based antibody maturation algorithm (MPTMA) is put forward. Antibody and antigen matching principle is analyzed in morphological space, it's proved that antibody set and redundancy are efficiently decreased by MPTMA, and antibody diversity is maintained, detection rate is improved. MPTMA is proved by theory and simulations that detection rate is improved and time complexity id decreased.
4. MPTMA with threshold prediction is applied to intrusion detection system (IDS), so a hybrid intrusion detection system (HIDS) is put forward. Minimal information disperse algorithm is adopted to disperse information and features of original data is extracted by PC A to implement IDS. This thesis puts forward hybrid intrusion detection system (HIDS) which compromise NSA based fast detectors and clonal selection algorithm based intelligent detectors, and the new features concluded by latter detectors are used to update database for better NSA based detection. The cooperation of the two kind detectors promises real-time and high detection rate. By simulation on our lib network, the better performance of brought up HIDS compared with NSA based IDS or clonal selection algorithm based IDS is testified in aspect of real-time, detection rate and false detection rate.
The main contributions of this thesis can be concluded as:
1. Aiming at premature problem of GA solving multi-objective optimizing problems, a cooperative co-evolution Messy GA is put forward. Global fitness function is defined, assistant operations are used, and chaotic mechanism is adopted to improve local search ability.
2. Aiming at local convergence problem of particle swarm optimization, penalty mechanism based crossover PSO is brought up. Self-adaptive crossover models are designed on basis of population convergence principle. Optimized parameters are calculated by designed formula to solve unimodal and multimodal optimizations.
3. For antibody maturation algorithm in artificial immune, a matching threshold prediction model is put forward, and tests prove that it overcomes optimal threshold difficulty. And multiple population GA based antibody maturation algorithm is brought up, whose detection rate is improved and time complexity is decreased.
4. MPTMA is applied as detector generation algorithm for IDS. Minimal information disperse algorithm and PCA feature extraction algorithm are adopted to realize IDS. A novel HIDS combining NSA based detectors and clonal selection algorithm based detectors is designed. Its performance in optimizing results, convergence results and stability are improved.
本文主要工作包括:
1.针对遗传算法求解多目标优化问题中存在的早熟问题,设计了一种染色体长度可变、混合编码的Messy遗传算法(Messy GA),并在此基础上提出全局适应度函数,实现了基于协作协进化的Messy GA(CCMGA)。在传统遗传算法的选择操作、交叉操作和变异操作基础上,利用简化操作、平滑操作和修复操作来辅助目标函数的优化。针对遗传算法容易丧失种群多样性的问题,结合混沌机制提高CCMGA的局部搜索能力。最后利用CCMGA实现多机器人路径规划,通过Matlab的仿真实验模拟多机器人在相对复杂的地图环境下完成动态路径规划,验证算法有效克服早熟问题,并且CCMGA能提高遗传算法的收敛速度和最优解。
2.粒子群算法近年出现了多种改进的方案,但均存在易陷入局部收敛的问题。本文提出一种基于惩罚机制的自适应交叉粒子群算法,有效克服局部收敛,并利用参数自适应解决单峰和多峰约束优化问题。根据粒子群进化过程中种群多样性模型,引入交叉操作,利用柯西不等式证明交叉粒子群算法通过保持种群多样性克服早熟和局部收敛,从而得到全局最优解。建立有限状态组成的马尔科夫链模型描述粒子群算法进化状态转换过程,有效控制粒子群算法收敛到全局最优,形成了自适应交叉粒子群算法。基于改进H策略和简化P策略惩罚机制,优化典型的Benchmark函数,分析实验结果得到:根据问题本身单峰和多峰的不同特性,参数设置影响收敛速度和最优解,因此本文提出参数自适应计算公式,有效提高粒子群算法求解单峰和多峰优化问题的性能。
3.针对人工免疫算法中抗体抗原最优阈值的求解困难,本文提出了匹配阈值预测模型,分析抗体抗原匹配规律,利用预测模型计算获得最优阈值,提高抗体检测效率。针对抗体生成算法复杂度高、生成抗体检测率低和抗体集合庞大的问题,提出了基于多种群遗传算法的抗体生成算法(MPTMA)。在形态学空间利用覆盖原理分析抗体抗原匹配,有效降低抗体集合的冗余度,减小抗体规模,保持抗体的多样性,提高抗体检测率。从理论和仿真分别证明MPTMA提高了抗体检测率、降低了抗体生成算法的时间复杂度。
4.将提出的基于阈值预测模型的MPTMA应用于入侵检测系统,提出了信息预处理机制。利用最小信息熵离散化算法对网络数据进行离散化处理,并结合PCA特征提取算法对数据进行特征提取。结合基于否定选择算法的快速匹配检测器和基于克隆选择算法的智能进化检测器,利用基于克隆选择算法的智能进化自学习得到的入侵特征更新前者的特征库,实现快速匹配检测器和基于克隆选择算法的智能进化检测器的协作,保证了混合检测器的检测实时性和准确性。仿真实验证明基于预测模型的MPTMA生成检测器提高了检测率,与传统的方法相比,在优化结果、收敛速度和稳定性上均有明显提高;同时相对单独使用上述两种检测器,混合检测系统在实时性、检测率和误测率等方面具备更好的性能。
本文的主要创新点:
1.针对遗传算法求解多目标优化存在早熟的问题,提出了基于协作协进化机制的Messy GA,构建全局适应度函数,利用辅助算子优化,并结合混沌机制提高局部搜索能力。
2.针对粒子群算法易陷入局部收敛问题,提出基于惩罚机制的交叉粒子群算法,分析种群收敛规律提出自适应交叉概率模型,求解单峰和多峰优化问题实现参数自适应,有效克服局部收敛,提高优化性能。
3.提出匹配阈值的预测模型,克服最优阈值的求解困难,在此基础上提出基于多种群遗传算法的抗体生成算法,在形态学空间利用覆盖原理分析抗体抗原匹配,MPTMA提高抗体检测率、降低抗体生成算法的时间复杂度。
4.将MPTMA作为入侵检测系统的检测器生成算法,利用最小信息熵离散化算法和PCA特征提取算法预处理信息,提出了结合基于否定选择算法的快速匹配检测器和基于克隆选择算法的智能进化检测器的混合检测器,在优化结果、收敛速度和稳定性上提高性能。
Bio-inspired computing, enlightened by natural intelligence of biological world, is a novel science for research and development of intelligent computing models and algorithms. Bio-inspired computing, including genetic algorithm, particle swarm optimization, artificial immune algorithm, ant clonal algorithm, neural network algorithm and etc., is considered as efficient optimizing algorithms widely applied in areas as artificial intelligence, machine learning, data mining, robots, network intrusion detection and etc. It provides novel solutions for complex problems. Compared with matured sciences, bio-inspired computing is still young which needs further discuss and research. In order to improve the efficiency, this paper is focused on three classic Bio-inspired computing algorithms: genetic algorithm, particle swarm optimization and artificial immune computing. CCMGA, penalty mechanism based crossover PSO and MPTMA are brought up, and the put forward algorithms are applied to multiple robots path planning and intrusion detection system to testify their efficiency. This paper provides novel ideas and methods for solving premature, local convergence and algorithm complexity problems in Bio-inspired computing.
The main work of this thesis can be concluded as:
1. Aiming at premature problem of GA optimizing multiple objective problems, drawbacks of traditional coding and fitness function definition in GA are pointed out. Messy GA with variable length of chromosome and hybrid coding is brought up, based on which a global fitness function is defined to implement cooperative co-evolution Messy GA (CCMGA). Besides operations of selection, crossover and mutation in traditional GA, simplify, smooth and repair operators are adopted to assist optimizing objective functions. Aiming at population diversity lost in GA, chaotic mechanism is applied to improve local search ability of CCMGA. Finally CCMGA is applied for multiple robots path planning. Matlab simulation results testify that multiple robots are able to optimize paths in various complicated maps. CCMGA is proved be capable of overcoming premature problem in GA, on basis of which convergence speed and optimized results are improved.
2. Particle swarm optimization (PSO) has obvious shortcoming: local convergence. In this paper, penalty mechanism based self-adaptive crossover PSO is put forward to overcome local convergence problem Based on population diversity model in evolutionary process for particles, crossover operation applied into PSO proved by Cauchy inequality is used to maintain population diversity to overcome problem of premature and local convergence and achieve global optimum solution. Evolutionary state transition process is depicted by Markov model consisted with finite-states, and self-adaptive crossover PSO is implemented. Penalty mechanism based Self-adaptive crossover PSO is designed for solving constrained optimization problems. Based on improved H strategy and simplified P strategy, experiments on benchmark functions demonstrate that parameters affect performance when optimizing unimodal and multimodal problems. A general calculating formula is put forward to control parameters for optimizing unimodal and multi-modal function optimizations respectively, which overcomes the in prior parameter setting difficulty.
3. Aiming at optimal matching threshold of antibody generation, a matching threshold prediction model is brought up in this paper. Analyzing antigen and antibody matching principle, optimized threshold is calculated by prediction model. In order to decrease complexity of antibody maturation algorithm, improve detection rate and smaller antibody set, multiple population GA based antibody maturation algorithm (MPTMA) is put forward. Antibody and antigen matching principle is analyzed in morphological space, it's proved that antibody set and redundancy are efficiently decreased by MPTMA, and antibody diversity is maintained, detection rate is improved. MPTMA is proved by theory and simulations that detection rate is improved and time complexity id decreased.
4. MPTMA with threshold prediction is applied to intrusion detection system (IDS), so a hybrid intrusion detection system (HIDS) is put forward. Minimal information disperse algorithm is adopted to disperse information and features of original data is extracted by PC A to implement IDS. This thesis puts forward hybrid intrusion detection system (HIDS) which compromise NSA based fast detectors and clonal selection algorithm based intelligent detectors, and the new features concluded by latter detectors are used to update database for better NSA based detection. The cooperation of the two kind detectors promises real-time and high detection rate. By simulation on our lib network, the better performance of brought up HIDS compared with NSA based IDS or clonal selection algorithm based IDS is testified in aspect of real-time, detection rate and false detection rate.
The main contributions of this thesis can be concluded as:
1. Aiming at premature problem of GA solving multi-objective optimizing problems, a cooperative co-evolution Messy GA is put forward. Global fitness function is defined, assistant operations are used, and chaotic mechanism is adopted to improve local search ability.
2. Aiming at local convergence problem of particle swarm optimization, penalty mechanism based crossover PSO is brought up. Self-adaptive crossover models are designed on basis of population convergence principle. Optimized parameters are calculated by designed formula to solve unimodal and multimodal optimizations.
3. For antibody maturation algorithm in artificial immune, a matching threshold prediction model is put forward, and tests prove that it overcomes optimal threshold difficulty. And multiple population GA based antibody maturation algorithm is brought up, whose detection rate is improved and time complexity is decreased.
4. MPTMA is applied as detector generation algorithm for IDS. Minimal information disperse algorithm and PCA feature extraction algorithm are adopted to realize IDS. A novel HIDS combining NSA based detectors and clonal selection algorithm based detectors is designed. Its performance in optimizing results, convergence results and stability are improved.
引文
[1]Moshe Sipper.Machine Nature: The coming age of bio-inspired computing [M].New York:McGraw-Hill Press, 2002.
[2]Teuscher C, Mange D., Stauffer A., Tempesti G.Bio-inspired computing tissues: towards machines that evolve, grow and learn [A].Elsevier Press[C].2003, 68(2): 235-244.
[3]Minsky, Paper.An introduction to computational geometry [M].Massachusetts: MIT Press, 1969.
[4]Kirkpatrick, S., C.D.Gelatt Jr., M.P.Vecchi.Optimization by simulated annealing [J].Science, 1983,220(4598): 671-680.
[5]John H.Holland.Genetic algorithms [DB].http://www.econ.iastate.edu/tesfatsi/hollan.GAIntro.htm,2005.
[6]Marc Schoenauer.Evolutionary computation [M].Massachusetts: MIT press, 1993.
[7]Kennedy J., Eberhart R.Particle swarm optimization [A].Proc.IEEE int'l conf.on neural networks[C].IEEE service center, Piscataway, NJ, 1995.Vol.IV: 1942-1948.
[8]E.Taillard.Robust tabu search for the quadratic assignment problem [J].Parallel Computing, 1991, 17:443-455.
[9]Aydin, Karakose M., Akin E.Artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm methods [A].Computational Intelligence for Measurement and Applications 2008[C], CIMSA 2008, 2008 IEEE International Conference on.July 14-16 2008: 93-98.
[10]Catania, V; Ficili, G; Panno, D.An assessment of resource exploitation using artificial intelligence based traffic control strategies [C].Computers and Communications, 1997.Proceedings.Second IEEE Symposium on 1-3 July 1997: 162-166.
[11]Gao Xueyao, Sun Liquan, Sun Dasong.Artificial immune-chaos hybrid algorithm for geometric constraint solving [J].Information technology Jouranl, 2009 3(2): 56-62.
[12]Oswaldo Ludwing Jr., Urbano Nunes.Applications of information theory, genetic algorithms and neural models to predict oil flow [J].Communications in Nonlinear Science and Numerical Simulation.July 2009, 14(7): 2870-2885.
[13]Ibeas, A.; dela Sen, M.Artificial intelligence techniques for designing switched discrete adaptive controllers for linear time invariant plants Systems [A].Man and Cybernetics, 2005 IEEE International Conference on [C].10-12 Oct.2005.4(4): 3504-3509.
[14]Braudaway, D.W.How artificial intelligence has helped and hindered calibration [A].Virtual and Intelligent Measurement Systems[C]2001, IEEE International Workshop on.VIMS 2001.19-20 May 2001:86-90.
[15]Catania, V; Ficili, G; Panno, D.An assessment of resource exploitation using artificial intelligence based traffic control strategies [A].Computers and Communications[C], 1997.Proceedings Second IEEE Symposium on 1-3 July 1997: 162-166.
[16]Dan, Y.Possibility of human grid computing for artificial intelligence systems [A].Applications and the Internet[C], 2008.SAINT 2008.International Symposium on July 28-Aug.1 2008: 452-454.
[17]彭京.基于生物启发计算的知识发现关键技术研究与实现[D].成都,四川大学硕士学位论文,2006.
[18]Yoseph Bar Cohen.Biomimietics:biologically inspired technologies[M],Taylor and Francis group publish,October 2005.
[19]Penro R.The Emperor's new mind:concerning computers,minds,and the laws of physics[M].New York:Oxford University Press,1989.
[20]Penrose R.Shadows of the minds,A search for the missing science of consciousness[M],New York:Oxford University Press,1944.
[21]黄席批,张著洪,何传江,胡小兵,马笑潇.现代智能算法理论及应用[M].科学出版社,2005.
[22]蔡自兴,徐光枯,人工智能及其应用(第三版)[M],清华大学出版社,2004,8.
[23]Chiu Chuiyu,Kuo Iting,Lin Chiahao.Applying artificial immune system and ant algorithm in air-conditioner market segmentation[J].Expert Systems with Applications Part 1.April 2009,36(3):4437-4442
[24]Chu Chien wei,Lin Minder,Liu Geefon,SHing Yung,Application of immune algorithms on solving minimum-cost problem of water distribution network[J].Mathematical and Computer Modelling,December 2008,48(11-12):1888-1900.
[25]Patil,N.;Chhaya Das;Shreya Patankar;Pol,K..Analysis of distributed intrusion detection systems using mobile agents[A].Emerging Trends in Engineering and Technology[C],2008.ICETET'08.First International Conference on 16-18 July 2008:1255-1260.
[26]M.R.Alrashidi,M.E.EI-Hawary.Applications of computational intelligence techniques for solving the revived optimal power flow problem[J].Electric Power Systems Research,April 2009,79(4):694-702.
[27]Gao Wei,Zhao Hai,Xu Jiuqiang,Song.Chunhe.A dynamic mutation PSO algorithm and its application in the neural networks[A].Intelligent Networks and Intelligent Systems[C],2008.ICINIS '08.First International Workshop on 1-3 Nov.2008:103-106.
[28]Chen Bingrui,Feng Xiating.Self-Adapting chaos-genetic hybrid algorithm with mixed congruential method[A].Natural Computation[C].2008.ICNC'08.Fourth International Conference on 18-20 Oct.2008,7:674-677.
[29]Supratid,I.M.A multi-subpopulation particle swarm optimization:a hybrid intelligent computing for function optimization[A].Natural Computation[C],2007.ICNC 2007.Third International Conference on 24-27 Aug.2007,(5):679-684.
[30]Chen Junfeng,Ren Ziwu,Fan Xinnan.A hybrid optimized algorithm based on improved simplex method and particle swarm optimization[A].Control Conference[C],2006.CCC 2006.Chinese 7-11Aug.2006:1448-1453.
[31]Dimitri C.Dracopoulos.Review of“genetic algorithms for machine learning by John J.Greffenstette”[M].Kluwer Academic Publishers,1993.9:32-45.
[32]Byung-joo Kim,Il-kon Kim.Kernel based intrusion detection system[A].Computer and Information Science[C],2005.Fourth Annual ACIS International Conference on 2005:13-18.
[33]Zhou,C.V.,Karunasekera,S.Leckie,C.Evaluation of a decentralized architecture for large scale collaborative intrusion detection[A].Integrated Network Management[C],2007.IM'07.10th IFIP/IEEE International Symposium on May 21 2007-Yearly 25 2007:80-89.
[34]Chureemart,J.,Churueang,P.Sensitivity analysis and its applications in power system improvements [A].Electrical Engineering/Electronics[C],Computer Telecommunications and Information Technology,2008.ECTI-CON 2008.5th International Conference on 14-17 May 2008,2:945-948.
[35]Chen Haiyan,Chen Jinfu,Duan Xianzhong.Multi-stage dynamic optimal power flow in wind power integrated system[A].Transmission and Distribution Conference and Exhibition[C]:Asia and Pacific,2005 IEEE/PES 2005:1-5.
[36]Hur D.,Jong-Keun Park,Kim B.H.,Kwang-Myoung Son.Security constrained optimal power flow for the evaluation of transmission capability on Korea electric power system[A].Power Engineering Society Summer Meeting[C],2001.IEEE,15-19 July 2001 2(2):1133-1138.
[37]S S Roy,D K Pratihar.A genetic-fuzzy approach for optimal path-planning of a robotic manipulator among static obstacles[J].IE(I) Journal.CP,2003:84-90.
[38]M.Erdmann,T.Lozano-Perez.On multiple moving objects[J].Algorithmica,1996,2(4):477-521.
[39]James P Anderson.Computer security threat monitoring and surveillance.Technical report[R].Fort Washington,Pennsylvania,April 1980:34-60.
[40]Gan Zhaohui,Zhao MingBo,.Chow W.S.Induction machine fault detection using clone selection programming[J].Expert Systems with Applications,May 2009,36(4):8000-8012.
[41]A.Merve Acilar,Ahmet Arslan.A collaborative filtering method based on artificial immune network [J].Expert Systems with Applications,May 2009,36(4):8324-8332.
[42]Maria G.Palacios,Joan E.Cunnick,David Vleck,Carol M.Vleck.Ontogeny of innate and adaptive immune defense components in free-living tree swallows[J],Tachycineta bicolor Developmental &Comparative Immunology,April 2009,33(4):456-463.
[43]Gonz(?)lez F.,A study of artificial immune systems applied to anomaly detection[D].The University of Memphis,Doctorial Dissertation,May,2003.
[44]庄健,王孙安.自调节遗传算法的研究[J].西安交通大学学报,2003,36(1):359-363.
[45]张铃,张钱.统计遗传算法[J].软件学报,1997,8(5):335-344.
[46]张铃.张钱.遗传算法机理的研究[J].软件学报.,2000,11(7),pp.945-952.
[47]张文修,梁怡.遗传算法的数学基础[M]..西安:西安交通大学出版社,2001:54-79.
[48]Hollan J H.Genetic algorithm and the optimal allocation of trials[J].SIAM Journal of Computing,1973,2:88-105.
[49]Forrest S.Genetic algorithms-principles of natural selection applied to computation[J]Science,1993,261(5123):872-878.
[50]Wang L,Zheng DZ.A modified evolutionary programming for flow shop scheduling[J].International Journal Advanced manufacturer Technology 2003,22(7-8):522-527.
[51]Ying KC,Liao C.J.An ant colony system for permutation flow shop sequencing[J].Computer Operation 2004,31:791-801.
[52]Wang L,Zheng DZ.An effective hybrid heuristic for flow shop scheduling[J].International Journal of Advanced Manufacturer Technology,2003,21(1):38-44.
[53]Wang L.Intelligent optimization with applications[M].Beijing:Tsinghua University Press,2004.
[54]Michalewicz Z.Genetic algorithms+data structures=evolution programs[M].New York:Springer,Berlin Heidelberg.1996.
[55]Holland J.H.Adaptation natural and artificial systems[M].University of Michigan press,1975.
[56]周明,孙树栋,遗传算法原理及应用[M].北京:国防工业出版社,1991.
[57]张莉芬,黎明,周琳霞.浮点数与格雷二进制混合编码的遗传算法[J].南昌航空工业学院学报,2001(6):17-30.
[58]杨晓华,杨志峰.格雷码混合加速遗传算法及其性能分析[J].北京师范大学学报(自然科学版),2004(12):831-836.
[59]杨晓华,陆桂华,杨志峰.格雷码加速遗传算法及其理论研究[J].系统工程理论与实践,2003(3):100-106.
[60]于有名,刘玉树,阎光伟.遗传算法的编码理论与应用[J].计算机工程与应用,2006(3):86-89.
[61]廖平,喻寿益.基于归一化实数编码遗传算法的圆锥度误差计算[J].仪器仪表学报,2004(6):234-238.
[62]方丹,王茹,林辉.基于实数编码的多算子演化遗传算法[J].计算机工程与应用,2004(13):87-90.
[63]沈艳军,汪秉文.基于实数编码的克隆选择算法及其应用[J].华中科技大学学报(自然科学版).2004,(2):41-42.
[64]M.L.Michelson,Phase equilibrium calculations:what is easy and what is difficult? Compute[J].Chemical.Engineer,1993,17(5/6):431-439.
[65]W.B.White,S.M.Johnson,G.B.Dantzig.Chemical equilibrium in complex mixtures[J].Chemical Physics.1958,28(5):751-756.
[66]Y Shi,R.C.Eberhart.A modified swarm optimizer[A].IEEE Internation Conference of Evolutionary Computation[C],Anchorage,Alaska:IEEE Press,May,1998:69-73.
[67]Clerc,M.The swarm and the queen:towards a deterministic and adaptive particle swarm optimization [A].Evolutionary Computation,1999.CEC 99[C].Proceedings of the 1999 Congress on 1999 3:1951-1957.
[68]Shi,Y.,Eberhart R.Fuzzy adaptive particle swarm optimization[A].IEEE Int.Conf.on Evolutionary Computation[C].Seoul,Korea,2001:101-106.
[69]高永超,智能优化算法的性能及搜索空间研究[M].山东:山东大学博士学位论文.2007.5.20:34-36.
[70]成飙,两种随机优化算法的改进及其化工应用研究[M].杭州:浙江大学博士学位论文,2007.6.1:23-26.
[71]Xiaohui Hu,Eberhart R.Multiobjective optimization using dynamic neighborhood particle swarm optimization[A].Proceedings of the 2002 Congress on Evolutionary Computation[C],2002(2):1677-1681.
[72]Van den Bergh F,Engel hrecht A P.Training product unit networks using cooperative particle swarm optimizers[A].International Joint Conference on Neural Networks[C].2001,Proceedings,2001(1):126-131.
[73]Van den Bergh F,Engel hrecht A P.Effects of swarm size on cooperative particle swarm optimizers.[A].Proc of the third Genetic and Evolutionary Computation Conference(GECCO)[C].San Francisco,USA,2001:892-899.
[74]Konstantinos E.Paropoulos,Michael N.On the computation of all global minimizers through particle swarm optimization[A].IEEE Transaction on Evolutionary Computation[C].2004,8(3):211-224.
[75]Wen Zhang,Yutian Liu.Reactive power optimization based on PSO in a practical power system[A].IEEE power engineering society general meeting[C].2004(1):239-242.
[76]Fukuyama Y Yoshida H.A particle swarm optimization for reactive power and voltage control in electric power systems [A].Proceedings of the 2001 Congress on Evolutionary Computation [C].2001 (1): 87-93.
[77]Alberto Garcia, Rafael Pastor.Introducing dynamic diversity a discrete particle swarm optimization [R].Computers and Operations Research, March 2009, 36(3): 951-966.
[78]Gonzalez F., A Study of Artificial Immune Systems Applied to Anomaly Detection [D].The University of Memphis Doctorial Dissertation, May, 2003.
[79]J.D.Farmer, N.Packard and A.Perelson.The immune system, adaptation and machine learning [J].Physica D, 1986,2:187-204.
[80]Han Zhenxiang, Jiang Quanyuan, Cao Yijia.Sequential feasible optimal power flow in power systems [J].Science in China Series E-Technological Sciences.2009, 52(2): 429-435.
[81]J.H.Hollan.Adaptive in natural and artificial systems [M].MIT Press.1992.
[82]D.E.Goldberg.Genetic algorithm in search, optimization and machine learning [M].Addison-Wesley Publishing Company, 1989.
[83]R.Ganjehmarzy, M.Davoody.Optimization of circular ring microstrip antenna using genetic algorithm [A], cnsr, pp., 2008 Communication network and services research conference (CNSR 2008) [C].2008: 222-227.
[84]Po Lewis.A genetic algorithm for maximum-likelihood phylogeny inference using nucleotide sequence data [J].Molecular biology and evolution, 2008, 15(3): 277-283.
[85]Frank J.Villegas, Tom Cwik, Yahya Rahmat Samii, Majid Manteghi.A parallel electromagnetic genetic algorithm optimization (EGO) application for path antenna design [A], IEEE transactions on antennas and propagation [C].September 2004, 53(9):1209-1213.
[86]Frank Lingelbach, Path planning using probabilistic cell decomposition [D].KTH Signaler, Sensorer och System, Doctorial Dissertation Sweden, 2005.
[87]Youssef Saab, Michael VanPutte.Shortest path planning on topographical maps [A].IEEE.Trans, on Systems, Man and Cybernetics, Part A: Systems and Humans [C].Jan.1999, 29(1): 139-150.
[88]Jerome Barraquand, Bruno Langlois, Jean-Claude Latombe.Numerical potential field techniques for robot path planning [A], IEEE Trans, on Systems, Man and Cybernetics [C].March/April 1991, 22(2): 224-241.
[89]Yong K.Hwang, Narendra Ahuja.A potential field approach to path planning [A], IEEE Trans, on Robotics and Automation [C].Feb.1992, 8(1): 23-32.
[90]Anthony Stentz.The focussed D* algorithm for real-time replanning [A].In Proc.of the Int.Joint Conf.on Artificial Intelligence [C].Aug.1995: 1652-1659.
[91]William D.Smart, Leslie Pack Kaelbling.Effective reinforcement learning for mobile robots [A].Proc.of the 2002 IEEE Int.Conf.on Robotics & Automation [C].May 2002: 2323-2339.
[92]Woong-Gie Han, Seung-Min Baek, Tae-Yong Kuc.GA based on-line path planning of mobile robots playing soccer games [A], in Proc.IEEE 40th Midwest Symp.Circuit Syst.[C].Sacramento, CA,Sept.1998: 522-525.
[93]Ahmed Elshamli, Mobile robots path planning optimization in static and dynamic environment [D].The University of Guelph, Doctorial Dissertation.2004: 12-30.
[94]Geisler, T., Manikas, T.Autonomous robot navigation system using a novel value encoded genetic algorithm [A].Proc.of IEEE Midwest Symp.on Circuits and Systems [C].Tulsa, 2002: 45-48.
[95]Wang Xiaoping, Cao Liming.Genetic algorithm theory and application [M].Shanghai: Xian Jiaotong University press, Jan.2002.
[96]B.D.Hutt, K.Warwick.Synapsing variable length crossover: biologically inspired crossover for variable length genomes [A].Proc.of the Sixth Int.Conf.on Artificial Neural Nets and Genetic Algorithms (ICANNGA 2003) [C].Springer-Verlag, 2003: 198-202.
[97]Shangming Wei, Milos Zefran.Smooth path planning and control for mobile robots [A].IEEE Int.Conf.on Networking, Sensing and Control [C].Tucson, Az, 2005: 23-30.
[98]Roland Philippsen, Roland Siegwart.Smooth and efficient obstacle avoidance for a tour guide robot [A].In Proc.of IEEE, Int.Conf.on Robotics and Automation [C].ICRA 2003: 446-451.
[99]Carlos Alfaro, M.Isabel Ribeiro, Pedro Lima.Smooth local path planning for a mobile manipulator [A].Proc.of the Scientific Meeting of the 4th Portuguese Robotics Festival [C].Proto, Portugal, April 2004: 1265-1270.
[100]Anmin Zhu, Simon X.Yang.Path planning of multi-robot systems with cooperation [A].Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation [C].Kobe, Japan, July 16-20,2003: 2301-2311.
[101]Piao Songhao, Hong Bingrong.A path planning approach to mobile robots under dynamic environment [J].Robot, Jan.2003,25(1 ):98-103.
[102]Qin Yuanqing, Sun Debao, Li Ning, Ma Qiang.Path planning for mobile robot based on particle swarm optimization [J].Robot, May, 2004, 26(3): 11-17.
[103]Zhang Rubo, Guo Bixiang, Xiong Jiang.Research on global path planning for robots based on ant colony algorithm [J].Journal of Harbin Engineering University, Dec.2004, 25(6): 23-24.
[104]Yang Dongyong.Multi-robot cooperation bBased-on learning and evolution [D].Zhejiang University,Master Dissertation.2005.
[105]Yang bo, Song yaoliang.A new chaos genetic algorithm and its application in the multicast routing [J].Journal of Nanjing University of Science and Technology, 2004, 28(1): 29-37.
[106]Paul W R, William C L, De Jong K A.An empirical analysis of collaboration methods in cooperative co-evolutionary algorithms [J].Journal Spector, 2002, 15:1235-1242.
[107]Wang Mei, Wu Tiejun.Cooperative co-evolution based distributed path planning of multiple mobile robots [J].Journal of Zhejiang University SCIENCE, 2005 6A (7): 697-706.
[108]Saroj Kumar Pradhan, Dayal Ramakrushna Parhi, Anup Kumar Panda.Fuzzy logic techniques for navigation of several mobile robots [J].Applied Soft Computing, January 2009,9(1): 290-304.
[109]Fernando Torres, Santiago Puente, Carolina Diaz.Automatic cooperative disassembly robotic system: Task planner to distribute tasks among robots [J].Control Engineering Practice, January 2009, 17(1):112-121.
[110]赵峰．动态环境卜移动机器人的路径规划[D]．北京：北京工业大学硕士学位论文,2003．
[111]Kennedy J, Eberhart R C.Particle swarm optimization [A].Proc IEEE International Conf on Neural Networks[A].Perth: IEEE Piscataway [C].1995: 1942-1948.
[112]Yuhui Shi, Russell Eberhart.A modified particle swarm optimizer [A].IEEE World Congress on Computational Intelligence[C].Anchorage: IEEE, 1998: 69-73.
[113]Chen Guimin, Jia Jianyuan, Han Qi.Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm [J].Journal of xi'an jiaotong university, Jan, 2006,40(1): 53-61.
[114]Qiang Zhao, Shaoze Yan.Collision-free path planning for mobile robots using chaotic particle swarm optimization [A].ICNC 2005 [C].LNCS 3612: 632-635.
[115]J.J.Liang, A.K.Qin.Comprehensive learning particle swarm optimizer for global optimization of multimodal functions [A].IEEE Transaction on Evolutionary Computation [C].June 2006, 10(3):281-295.
[116]RIGET J, VESTERSTROEM J S.A diversity-guided particle swarm optimizer-the ARPSO [R].Aarhus: University of Aarhus, 2002: 23-30.
[117]Y.J.Cao, Q.H.Wu.Convergence analysis of adaptive genetic algorithm, genetic algorithms in engineering systems [J].Innovations and applications,2-4 September 1997: 446-450.
[118]Xihuai Wang, Junjun Li.Hybrid particle swarm optimization with simulated annealing [A].Proceedings of the third international conference on machine learning and cybernetics [C].Shanghai,26-29, August 2004: 1109-1113.
[119]Asanga ratnawera, Saman K.Halgamuge, Self-organizing hierarchical partical swarm optimizer with time-varying acceleration coefficients [A].IEEE transactions on evolutionary computation [C].June 2004, 8(3): 240-256.
[120]Suganthan, P.N, Particle swarm optimizer with neighbourhood operator [A].Evolutionary computation [C].1999, CEC99, 6-9 July 1999, 3: 1958-1962.
[121]Clerc.M, The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space [A].Evolutionary computation, IEEE transactions on [C].Feb.2002, 6(1): 58-73.
[122]Azuma Ide, Keiichiro yasuda.A basic study of the adaptive particle swarm optimization [A].IEEJ transition [C].2004, 124(2): 550-557.
[123]Pulido GT., Coello, C.A.C.A constraint-handling mechanism for particle swarm optimization [A].Evolutionary computation [C].2004.CEC2004.19-23 June 2004: 456-462.
[124]Lei Kaiyou, Qiu Yuhui.A study of constrained layout optimization using adaptive particle swarm optimizer [J].Journal of computer research and development, 2006,10: 33-40.
[125]Karin Zielinski, Rainer Laur.Constrained single-objective optimization using particle swarm optimization [A].IEEE Congress on evolutionary computation [C].July 16-21,2006: 101-109.
[126]Parsopoulos K E, Vrahatis M N.Particle swarm optimization method for constrained optimization problems[A].Proceedings of the 2002 Euro-International Symposium on Computation Intelligence [C].2002: 214-220.
[127]Graham Kendall, Yan Su.A particle swarm optimization approach in the construction of optimal risky portfolios [A].Proceedings of the 23~(rd) IASTED international multi-conference artificial intelligence and applications [C].Innsbruck, Austria Feb 14-16, 2005:1235-1241.
[128]Wang Yong, Cai Zixing, Zhen Wei, Liu Hui.A new evolutionary algorithm for solving constrained optimization problems [J].Journal of central south university (science and technology), 2006, 37(1):119-123.
[129]Tetsuyuki Takahama, Setsuko Sakai.Constrained optimization by a constrained particle swarm optimizer [J].Journal of advanced computational intelligence and intelligent informatics, 2005, 9(3):282-289.
[130]Homaiffar A., Qi C, Lai S.Constrained optimization via genetic algorithms [J].Simulation, 1994, 62(4): 242-254.
[131]Joines, J, Houck, C.On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's [A].Proceedings of first IEEE conference on evolutionary computation [C].1994: 579-584.
[132]Powell, D., Skoinick, M.Using genetic algorithms in engineering design optimization with non-linear constraints [A].Proceedings of the fifth international conference on genetic algorithms [C].1993:270-271.
[133]F.van den Bergh.An analysis of particle swarm optimizers [D].University of Pretoria, Doctorial Dissertation.2001.
[134]Forrest S, Perelson A S, Allen L, et al.Self non-self discrimination in a computer [A].In Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy[C].Los Alamos CA IEEE Computer Society Press, 1994.
[135]D'haeseller P, Forrest S.An immunological approach to change detection: Algorithm, analysis and implication [A].In: Proc.of the IEEE Symp.On Research in Security and Privacy [C].Oakland:IEEE Computer Society Press, 1996:110-119.
[136]Fabio Gonzalez, Dipankar Dasgupta, Luis Fernando Nifio.A randomized real-valued negative selection algorithm [A].In Proceedings of the 2~(nd) International Conference on Artificial Immune Systems [C].Edinburgh, UK, September 2003: 261-272.
[137]Ji Z, Dasgupta D.Real-valued negative selection algorithm with variable-seized detectors[A].Genetic and Evolutionary Computation [C].Seattle: IEEE Press, 2004: 287-298.
[138]Wenjian Luo, Xin Wang, Ying Tan, Xufa Wang.A novel negative selection algorithm with an array of partial matching lengths for each detector [A].PPSN IX [C].LNCS 4193, 2006: 112-121.
[139]Mohammad Reza Ahmadi, Davood Maleki.A co-evolutionary immune system framework in a grid environment for enterprise network security [A].SSI 2006 [C].November 9th, 2006: 1136-1143.
[140]Jungan Chen, Feng Liang, Dongyong Yang.Dynamic negative selection algorithm based on match range model [A].LNCS [C].2005, 38(9): 1201-1208.
[141]Y.J.Cao, Q.H.Wu.Convergence analysis of adaptive genetic algorithms, Genetic algorithm in engineering systems: Innovations and applications [A].2-4 September 1997, Conference publication No.446, IEEE [C].1997: 2301-2309.
[142]Katja L, Rainer B, Tansu A, Achim M, Sahin A.A cooperative AIS framework for intrusion detection [A].IEEE Comminication society subject matter experts for publication in ICC 2007 proceedings [C].2007: 1409-1416.
[143]Shen He, Wenjian Luo, Xifa Wang.A negative selection algorithm with the variable length detector [J].Journal of Software, Vol.18, No.6, June 2007: 1361-1368.
[144]Jungwon Kim, Peter J.Bentley.An evaluation of negative selection in an artificial immune system for network intrusion detection [A].Proceedings of the Genetic and Evolutionary Conference (GECCO-2001)[C].2001: 1330-1337.
[145]Xiaoli Xiao, Yuehong Tian, Chuan Chen.Anomaly detection based on improved negative selection matching algorithm [J].Computer Applications, Feb, 2005, 25(2): 383-385.
[146]Zhou Ji, Dipankar Dasgupta.Real-valued negative selection algorithm with variable-sized detectors [A].Genetic and Evolutionary Computation [C].Washington: IEEE Press, 2005: 88-97.
[147]Kim J, Bentley P.The human immune system and network intrusion detection [A].7~(th) European Congress on Intelligent Techniques and Soft Computing (EURIT' 99) [C].1999:1909-1919.
[148]Luther K, Bye R, Alpcan T, Muller A.A cooperative AIS framework for intrusion detection [A].ICC 07, IEEE International Conference on Communications [C].24-28 June 2007:1409-1416.
[149]Jungwon Kim, Peter J.Bentley, Uwe A, Julie G Immune system approaches to intrusion detection-a review[A].In: ICARIS-2004, 3~(rd) International Conference on Artificial Immune Systems [C].LNCS 3239,2004:316-329.
[150]Wenjian Luo, Xianbin Cao, Xufa Wang.Research on adaptively generating detector algorithm [J].Acta Automatica Sinica, 2005.11, 31(6): 907-916.
[151]Tao Li.An immune based dynamic intrusion detection model [J].Chinese Science Bulletin, 2005 (22): 2650-2657.
[152]Tao Li.Computer Immunology [M].Publishing house of electronics industry, 2004.7.
[153]Jungan Chen.Intrusion detector maturation algorithm based on artificial immune system [D].Zhejiang: Zhejiang University of Technology, 2004, pp.43-48.
[154]Roberto, Di Pietro, Luigi V.Mancini.Intrusion detection system [A].Springer-Verlag New York Inc.[C].August 1st, 2008: 2301-2317.
[155]J.Redish.Expanding usability testing to evaluate complex system [J].Journal of usability studies,2007 2(3): 102-111.
[156]R.Werlinger, K.Hawkey, K.Benznosov.Security practitioners in context: their activities are iterations [A].In CHI'08 extended abstracts on human factors in computing system [C].2008:3789-3794.
[157]Dong Seong Kim, Jong Sou Park.Modeling network intrusion detection system using feature selection and parameters optimization [A].IEICE transactions on information and systems [C].2008 E91-D(4): 1050-1057.
[158]A.Gagne, K.Muldner, K.Beznosov.Identifying differences between security and other IT professionals: a qualitative analysis [A].In proc.of human aspects of information security and assurance (HAISA) [C].Plymouth, England, July 2008: 1201-1209.
[159]Hunt, J.Timmis, D.Cooke, M.Neal, C, King.The development of an artificial immune system for real world application.Application of artificial immune systems [A].D.Dasgupta Ed, Pub.Springer-Verlag [C].ISBN 3-540-64390-7, 1999: 157-186.
[160]J.Twycross, U.Aickelin.Libtissue-implementing innate immunity [A].In the preoceedings of IEEE world congress on computational intelligence [C].Vancourver, Canada, July 17-21, 2006: 909-907.
[161]Zhang Ran.Research on key technology of dynamic adaptive intrusion detection [D].Xian Jiaotong University, Doctorial Dissertation.2003.
[162]Balazinska M, Merlo E, Dagenais M.Advanced clone-analysis to support object-oriented system refactoring [A].The 7 working conference on reverse engineering [C].Brisbane, Australia, 2000:709-713.
[163]Northcutt S, Novak J.Network intrusion detection: an analysis handbook [M].New Riders Press,2000.
[164]Chen U, Cheng XQ, Li Y, Dai L.Lightweight intrusion detection system based on feature selection [J].Journal of software, 2007, 18(7): 1639-1651.
[165]Qusay Mahmoud, Cognitive network: Towards self-aware networks [M].Wiley publisher, September 24th, 2007.
[166]James Dougherty, Ron Kohavi, Mehran Sahami.Supervised and unsupervised descretization of continuous features [A].Machine learning: Proceedings of the twelfth international conference [C].1995: 1209-1213.
[167]UM Fayyad, K B Irani.Multi-interval discretization of continuous-valued attributes for classification learning [A].In proceedings of the 13th internaitonl joint conference on artificial intelligence [C].1993:423-410.
[168]Dataset [DB]: http://www.ailab.si/orange/datasets.asp.2008.3.
[169]David Nguyen, Abhishek Das, Gokhan Memik, Alok Choudhary.A reconfigurable architecture for network intrusion detection using principal component analysis [A].Proceedings of the 2006 ACM/SIGDA 14~(th) international symposium on field programmable gate arrays [C].Feb.2006: 235-236.
[170]Jonathon Shlens.A tutorial on principal component analysis [R].Systems neurobiology laboratory,University of California, San Diego, December 10, 2005.
[2]Teuscher C, Mange D., Stauffer A., Tempesti G.Bio-inspired computing tissues: towards machines that evolve, grow and learn [A].Elsevier Press[C].2003, 68(2): 235-244.
[3]Minsky, Paper.An introduction to computational geometry [M].Massachusetts: MIT Press, 1969.
[4]Kirkpatrick, S., C.D.Gelatt Jr., M.P.Vecchi.Optimization by simulated annealing [J].Science, 1983,220(4598): 671-680.
[5]John H.Holland.Genetic algorithms [DB].http://www.econ.iastate.edu/tesfatsi/hollan.GAIntro.htm,2005.
[6]Marc Schoenauer.Evolutionary computation [M].Massachusetts: MIT press, 1993.
[7]Kennedy J., Eberhart R.Particle swarm optimization [A].Proc.IEEE int'l conf.on neural networks[C].IEEE service center, Piscataway, NJ, 1995.Vol.IV: 1942-1948.
[8]E.Taillard.Robust tabu search for the quadratic assignment problem [J].Parallel Computing, 1991, 17:443-455.
[9]Aydin, Karakose M., Akin E.Artificial immune inspired fault detection algorithm based on fuzzy clustering and genetic algorithm methods [A].Computational Intelligence for Measurement and Applications 2008[C], CIMSA 2008, 2008 IEEE International Conference on.July 14-16 2008: 93-98.
[10]Catania, V; Ficili, G; Panno, D.An assessment of resource exploitation using artificial intelligence based traffic control strategies [C].Computers and Communications, 1997.Proceedings.Second IEEE Symposium on 1-3 July 1997: 162-166.
[11]Gao Xueyao, Sun Liquan, Sun Dasong.Artificial immune-chaos hybrid algorithm for geometric constraint solving [J].Information technology Jouranl, 2009 3(2): 56-62.
[12]Oswaldo Ludwing Jr., Urbano Nunes.Applications of information theory, genetic algorithms and neural models to predict oil flow [J].Communications in Nonlinear Science and Numerical Simulation.July 2009, 14(7): 2870-2885.
[13]Ibeas, A.; dela Sen, M.Artificial intelligence techniques for designing switched discrete adaptive controllers for linear time invariant plants Systems [A].Man and Cybernetics, 2005 IEEE International Conference on [C].10-12 Oct.2005.4(4): 3504-3509.
[14]Braudaway, D.W.How artificial intelligence has helped and hindered calibration [A].Virtual and Intelligent Measurement Systems[C]2001, IEEE International Workshop on.VIMS 2001.19-20 May 2001:86-90.
[15]Catania, V; Ficili, G; Panno, D.An assessment of resource exploitation using artificial intelligence based traffic control strategies [A].Computers and Communications[C], 1997.Proceedings Second IEEE Symposium on 1-3 July 1997: 162-166.
[16]Dan, Y.Possibility of human grid computing for artificial intelligence systems [A].Applications and the Internet[C], 2008.SAINT 2008.International Symposium on July 28-Aug.1 2008: 452-454.
[17]彭京.基于生物启发计算的知识发现关键技术研究与实现[D].成都,四川大学硕士学位论文,2006.
[18]Yoseph Bar Cohen.Biomimietics:biologically inspired technologies[M],Taylor and Francis group publish,October 2005.
[19]Penro R.The Emperor's new mind:concerning computers,minds,and the laws of physics[M].New York:Oxford University Press,1989.
[20]Penrose R.Shadows of the minds,A search for the missing science of consciousness[M],New York:Oxford University Press,1944.
[21]黄席批,张著洪,何传江,胡小兵,马笑潇.现代智能算法理论及应用[M].科学出版社,2005.
[22]蔡自兴,徐光枯,人工智能及其应用(第三版)[M],清华大学出版社,2004,8.
[23]Chiu Chuiyu,Kuo Iting,Lin Chiahao.Applying artificial immune system and ant algorithm in air-conditioner market segmentation[J].Expert Systems with Applications Part 1.April 2009,36(3):4437-4442
[24]Chu Chien wei,Lin Minder,Liu Geefon,SHing Yung,Application of immune algorithms on solving minimum-cost problem of water distribution network[J].Mathematical and Computer Modelling,December 2008,48(11-12):1888-1900.
[25]Patil,N.;Chhaya Das;Shreya Patankar;Pol,K..Analysis of distributed intrusion detection systems using mobile agents[A].Emerging Trends in Engineering and Technology[C],2008.ICETET'08.First International Conference on 16-18 July 2008:1255-1260.
[26]M.R.Alrashidi,M.E.EI-Hawary.Applications of computational intelligence techniques for solving the revived optimal power flow problem[J].Electric Power Systems Research,April 2009,79(4):694-702.
[27]Gao Wei,Zhao Hai,Xu Jiuqiang,Song.Chunhe.A dynamic mutation PSO algorithm and its application in the neural networks[A].Intelligent Networks and Intelligent Systems[C],2008.ICINIS '08.First International Workshop on 1-3 Nov.2008:103-106.
[28]Chen Bingrui,Feng Xiating.Self-Adapting chaos-genetic hybrid algorithm with mixed congruential method[A].Natural Computation[C].2008.ICNC'08.Fourth International Conference on 18-20 Oct.2008,7:674-677.
[29]Supratid,I.M.A multi-subpopulation particle swarm optimization:a hybrid intelligent computing for function optimization[A].Natural Computation[C],2007.ICNC 2007.Third International Conference on 24-27 Aug.2007,(5):679-684.
[30]Chen Junfeng,Ren Ziwu,Fan Xinnan.A hybrid optimized algorithm based on improved simplex method and particle swarm optimization[A].Control Conference[C],2006.CCC 2006.Chinese 7-11Aug.2006:1448-1453.
[31]Dimitri C.Dracopoulos.Review of“genetic algorithms for machine learning by John J.Greffenstette”[M].Kluwer Academic Publishers,1993.9:32-45.
[32]Byung-joo Kim,Il-kon Kim.Kernel based intrusion detection system[A].Computer and Information Science[C],2005.Fourth Annual ACIS International Conference on 2005:13-18.
[33]Zhou,C.V.,Karunasekera,S.Leckie,C.Evaluation of a decentralized architecture for large scale collaborative intrusion detection[A].Integrated Network Management[C],2007.IM'07.10th IFIP/IEEE International Symposium on May 21 2007-Yearly 25 2007:80-89.
[34]Chureemart,J.,Churueang,P.Sensitivity analysis and its applications in power system improvements [A].Electrical Engineering/Electronics[C],Computer Telecommunications and Information Technology,2008.ECTI-CON 2008.5th International Conference on 14-17 May 2008,2:945-948.
[35]Chen Haiyan,Chen Jinfu,Duan Xianzhong.Multi-stage dynamic optimal power flow in wind power integrated system[A].Transmission and Distribution Conference and Exhibition[C]:Asia and Pacific,2005 IEEE/PES 2005:1-5.
[36]Hur D.,Jong-Keun Park,Kim B.H.,Kwang-Myoung Son.Security constrained optimal power flow for the evaluation of transmission capability on Korea electric power system[A].Power Engineering Society Summer Meeting[C],2001.IEEE,15-19 July 2001 2(2):1133-1138.
[37]S S Roy,D K Pratihar.A genetic-fuzzy approach for optimal path-planning of a robotic manipulator among static obstacles[J].IE(I) Journal.CP,2003:84-90.
[38]M.Erdmann,T.Lozano-Perez.On multiple moving objects[J].Algorithmica,1996,2(4):477-521.
[39]James P Anderson.Computer security threat monitoring and surveillance.Technical report[R].Fort Washington,Pennsylvania,April 1980:34-60.
[40]Gan Zhaohui,Zhao MingBo,.Chow W.S.Induction machine fault detection using clone selection programming[J].Expert Systems with Applications,May 2009,36(4):8000-8012.
[41]A.Merve Acilar,Ahmet Arslan.A collaborative filtering method based on artificial immune network [J].Expert Systems with Applications,May 2009,36(4):8324-8332.
[42]Maria G.Palacios,Joan E.Cunnick,David Vleck,Carol M.Vleck.Ontogeny of innate and adaptive immune defense components in free-living tree swallows[J],Tachycineta bicolor Developmental &Comparative Immunology,April 2009,33(4):456-463.
[43]Gonz(?)lez F.,A study of artificial immune systems applied to anomaly detection[D].The University of Memphis,Doctorial Dissertation,May,2003.
[44]庄健,王孙安.自调节遗传算法的研究[J].西安交通大学学报,2003,36(1):359-363.
[45]张铃,张钱.统计遗传算法[J].软件学报,1997,8(5):335-344.
[46]张铃.张钱.遗传算法机理的研究[J].软件学报.,2000,11(7),pp.945-952.
[47]张文修,梁怡.遗传算法的数学基础[M]..西安:西安交通大学出版社,2001:54-79.
[48]Hollan J H.Genetic algorithm and the optimal allocation of trials[J].SIAM Journal of Computing,1973,2:88-105.
[49]Forrest S.Genetic algorithms-principles of natural selection applied to computation[J]Science,1993,261(5123):872-878.
[50]Wang L,Zheng DZ.A modified evolutionary programming for flow shop scheduling[J].International Journal Advanced manufacturer Technology 2003,22(7-8):522-527.
[51]Ying KC,Liao C.J.An ant colony system for permutation flow shop sequencing[J].Computer Operation 2004,31:791-801.
[52]Wang L,Zheng DZ.An effective hybrid heuristic for flow shop scheduling[J].International Journal of Advanced Manufacturer Technology,2003,21(1):38-44.
[53]Wang L.Intelligent optimization with applications[M].Beijing:Tsinghua University Press,2004.
[54]Michalewicz Z.Genetic algorithms+data structures=evolution programs[M].New York:Springer,Berlin Heidelberg.1996.
[55]Holland J.H.Adaptation natural and artificial systems[M].University of Michigan press,1975.
[56]周明,孙树栋,遗传算法原理及应用[M].北京:国防工业出版社,1991.
[57]张莉芬,黎明,周琳霞.浮点数与格雷二进制混合编码的遗传算法[J].南昌航空工业学院学报,2001(6):17-30.
[58]杨晓华,杨志峰.格雷码混合加速遗传算法及其性能分析[J].北京师范大学学报(自然科学版),2004(12):831-836.
[59]杨晓华,陆桂华,杨志峰.格雷码加速遗传算法及其理论研究[J].系统工程理论与实践,2003(3):100-106.
[60]于有名,刘玉树,阎光伟.遗传算法的编码理论与应用[J].计算机工程与应用,2006(3):86-89.
[61]廖平,喻寿益.基于归一化实数编码遗传算法的圆锥度误差计算[J].仪器仪表学报,2004(6):234-238.
[62]方丹,王茹,林辉.基于实数编码的多算子演化遗传算法[J].计算机工程与应用,2004(13):87-90.
[63]沈艳军,汪秉文.基于实数编码的克隆选择算法及其应用[J].华中科技大学学报(自然科学版).2004,(2):41-42.
[64]M.L.Michelson,Phase equilibrium calculations:what is easy and what is difficult? Compute[J].Chemical.Engineer,1993,17(5/6):431-439.
[65]W.B.White,S.M.Johnson,G.B.Dantzig.Chemical equilibrium in complex mixtures[J].Chemical Physics.1958,28(5):751-756.
[66]Y Shi,R.C.Eberhart.A modified swarm optimizer[A].IEEE Internation Conference of Evolutionary Computation[C],Anchorage,Alaska:IEEE Press,May,1998:69-73.
[67]Clerc,M.The swarm and the queen:towards a deterministic and adaptive particle swarm optimization [A].Evolutionary Computation,1999.CEC 99[C].Proceedings of the 1999 Congress on 1999 3:1951-1957.
[68]Shi,Y.,Eberhart R.Fuzzy adaptive particle swarm optimization[A].IEEE Int.Conf.on Evolutionary Computation[C].Seoul,Korea,2001:101-106.
[69]高永超,智能优化算法的性能及搜索空间研究[M].山东:山东大学博士学位论文.2007.5.20:34-36.
[70]成飙,两种随机优化算法的改进及其化工应用研究[M].杭州:浙江大学博士学位论文,2007.6.1:23-26.
[71]Xiaohui Hu,Eberhart R.Multiobjective optimization using dynamic neighborhood particle swarm optimization[A].Proceedings of the 2002 Congress on Evolutionary Computation[C],2002(2):1677-1681.
[72]Van den Bergh F,Engel hrecht A P.Training product unit networks using cooperative particle swarm optimizers[A].International Joint Conference on Neural Networks[C].2001,Proceedings,2001(1):126-131.
[73]Van den Bergh F,Engel hrecht A P.Effects of swarm size on cooperative particle swarm optimizers.[A].Proc of the third Genetic and Evolutionary Computation Conference(GECCO)[C].San Francisco,USA,2001:892-899.
[74]Konstantinos E.Paropoulos,Michael N.On the computation of all global minimizers through particle swarm optimization[A].IEEE Transaction on Evolutionary Computation[C].2004,8(3):211-224.
[75]Wen Zhang,Yutian Liu.Reactive power optimization based on PSO in a practical power system[A].IEEE power engineering society general meeting[C].2004(1):239-242.
[76]Fukuyama Y Yoshida H.A particle swarm optimization for reactive power and voltage control in electric power systems [A].Proceedings of the 2001 Congress on Evolutionary Computation [C].2001 (1): 87-93.
[77]Alberto Garcia, Rafael Pastor.Introducing dynamic diversity a discrete particle swarm optimization [R].Computers and Operations Research, March 2009, 36(3): 951-966.
[78]Gonzalez F., A Study of Artificial Immune Systems Applied to Anomaly Detection [D].The University of Memphis Doctorial Dissertation, May, 2003.
[79]J.D.Farmer, N.Packard and A.Perelson.The immune system, adaptation and machine learning [J].Physica D, 1986,2:187-204.
[80]Han Zhenxiang, Jiang Quanyuan, Cao Yijia.Sequential feasible optimal power flow in power systems [J].Science in China Series E-Technological Sciences.2009, 52(2): 429-435.
[81]J.H.Hollan.Adaptive in natural and artificial systems [M].MIT Press.1992.
[82]D.E.Goldberg.Genetic algorithm in search, optimization and machine learning [M].Addison-Wesley Publishing Company, 1989.
[83]R.Ganjehmarzy, M.Davoody.Optimization of circular ring microstrip antenna using genetic algorithm [A], cnsr, pp., 2008 Communication network and services research conference (CNSR 2008) [C].2008: 222-227.
[84]Po Lewis.A genetic algorithm for maximum-likelihood phylogeny inference using nucleotide sequence data [J].Molecular biology and evolution, 2008, 15(3): 277-283.
[85]Frank J.Villegas, Tom Cwik, Yahya Rahmat Samii, Majid Manteghi.A parallel electromagnetic genetic algorithm optimization (EGO) application for path antenna design [A], IEEE transactions on antennas and propagation [C].September 2004, 53(9):1209-1213.
[86]Frank Lingelbach, Path planning using probabilistic cell decomposition [D].KTH Signaler, Sensorer och System, Doctorial Dissertation Sweden, 2005.
[87]Youssef Saab, Michael VanPutte.Shortest path planning on topographical maps [A].IEEE.Trans, on Systems, Man and Cybernetics, Part A: Systems and Humans [C].Jan.1999, 29(1): 139-150.
[88]Jerome Barraquand, Bruno Langlois, Jean-Claude Latombe.Numerical potential field techniques for robot path planning [A], IEEE Trans, on Systems, Man and Cybernetics [C].March/April 1991, 22(2): 224-241.
[89]Yong K.Hwang, Narendra Ahuja.A potential field approach to path planning [A], IEEE Trans, on Robotics and Automation [C].Feb.1992, 8(1): 23-32.
[90]Anthony Stentz.The focussed D* algorithm for real-time replanning [A].In Proc.of the Int.Joint Conf.on Artificial Intelligence [C].Aug.1995: 1652-1659.
[91]William D.Smart, Leslie Pack Kaelbling.Effective reinforcement learning for mobile robots [A].Proc.of the 2002 IEEE Int.Conf.on Robotics & Automation [C].May 2002: 2323-2339.
[92]Woong-Gie Han, Seung-Min Baek, Tae-Yong Kuc.GA based on-line path planning of mobile robots playing soccer games [A], in Proc.IEEE 40th Midwest Symp.Circuit Syst.[C].Sacramento, CA,Sept.1998: 522-525.
[93]Ahmed Elshamli, Mobile robots path planning optimization in static and dynamic environment [D].The University of Guelph, Doctorial Dissertation.2004: 12-30.
[94]Geisler, T., Manikas, T.Autonomous robot navigation system using a novel value encoded genetic algorithm [A].Proc.of IEEE Midwest Symp.on Circuits and Systems [C].Tulsa, 2002: 45-48.
[95]Wang Xiaoping, Cao Liming.Genetic algorithm theory and application [M].Shanghai: Xian Jiaotong University press, Jan.2002.
[96]B.D.Hutt, K.Warwick.Synapsing variable length crossover: biologically inspired crossover for variable length genomes [A].Proc.of the Sixth Int.Conf.on Artificial Neural Nets and Genetic Algorithms (ICANNGA 2003) [C].Springer-Verlag, 2003: 198-202.
[97]Shangming Wei, Milos Zefran.Smooth path planning and control for mobile robots [A].IEEE Int.Conf.on Networking, Sensing and Control [C].Tucson, Az, 2005: 23-30.
[98]Roland Philippsen, Roland Siegwart.Smooth and efficient obstacle avoidance for a tour guide robot [A].In Proc.of IEEE, Int.Conf.on Robotics and Automation [C].ICRA 2003: 446-451.
[99]Carlos Alfaro, M.Isabel Ribeiro, Pedro Lima.Smooth local path planning for a mobile manipulator [A].Proc.of the Scientific Meeting of the 4th Portuguese Robotics Festival [C].Proto, Portugal, April 2004: 1265-1270.
[100]Anmin Zhu, Simon X.Yang.Path planning of multi-robot systems with cooperation [A].Proceedings 2003 IEEE International Symposium on Computational Intelligence in Robotics and Automation [C].Kobe, Japan, July 16-20,2003: 2301-2311.
[101]Piao Songhao, Hong Bingrong.A path planning approach to mobile robots under dynamic environment [J].Robot, Jan.2003,25(1 ):98-103.
[102]Qin Yuanqing, Sun Debao, Li Ning, Ma Qiang.Path planning for mobile robot based on particle swarm optimization [J].Robot, May, 2004, 26(3): 11-17.
[103]Zhang Rubo, Guo Bixiang, Xiong Jiang.Research on global path planning for robots based on ant colony algorithm [J].Journal of Harbin Engineering University, Dec.2004, 25(6): 23-24.
[104]Yang Dongyong.Multi-robot cooperation bBased-on learning and evolution [D].Zhejiang University,Master Dissertation.2005.
[105]Yang bo, Song yaoliang.A new chaos genetic algorithm and its application in the multicast routing [J].Journal of Nanjing University of Science and Technology, 2004, 28(1): 29-37.
[106]Paul W R, William C L, De Jong K A.An empirical analysis of collaboration methods in cooperative co-evolutionary algorithms [J].Journal Spector, 2002, 15:1235-1242.
[107]Wang Mei, Wu Tiejun.Cooperative co-evolution based distributed path planning of multiple mobile robots [J].Journal of Zhejiang University SCIENCE, 2005 6A (7): 697-706.
[108]Saroj Kumar Pradhan, Dayal Ramakrushna Parhi, Anup Kumar Panda.Fuzzy logic techniques for navigation of several mobile robots [J].Applied Soft Computing, January 2009,9(1): 290-304.
[109]Fernando Torres, Santiago Puente, Carolina Diaz.Automatic cooperative disassembly robotic system: Task planner to distribute tasks among robots [J].Control Engineering Practice, January 2009, 17(1):112-121.
[110]赵峰．动态环境卜移动机器人的路径规划[D]．北京：北京工业大学硕士学位论文,2003．
[111]Kennedy J, Eberhart R C.Particle swarm optimization [A].Proc IEEE International Conf on Neural Networks[A].Perth: IEEE Piscataway [C].1995: 1942-1948.
[112]Yuhui Shi, Russell Eberhart.A modified particle swarm optimizer [A].IEEE World Congress on Computational Intelligence[C].Anchorage: IEEE, 1998: 69-73.
[113]Chen Guimin, Jia Jianyuan, Han Qi.Study on the strategy of decreasing inertia weight in particle swarm optimization algorithm [J].Journal of xi'an jiaotong university, Jan, 2006,40(1): 53-61.
[114]Qiang Zhao, Shaoze Yan.Collision-free path planning for mobile robots using chaotic particle swarm optimization [A].ICNC 2005 [C].LNCS 3612: 632-635.
[115]J.J.Liang, A.K.Qin.Comprehensive learning particle swarm optimizer for global optimization of multimodal functions [A].IEEE Transaction on Evolutionary Computation [C].June 2006, 10(3):281-295.
[116]RIGET J, VESTERSTROEM J S.A diversity-guided particle swarm optimizer-the ARPSO [R].Aarhus: University of Aarhus, 2002: 23-30.
[117]Y.J.Cao, Q.H.Wu.Convergence analysis of adaptive genetic algorithm, genetic algorithms in engineering systems [J].Innovations and applications,2-4 September 1997: 446-450.
[118]Xihuai Wang, Junjun Li.Hybrid particle swarm optimization with simulated annealing [A].Proceedings of the third international conference on machine learning and cybernetics [C].Shanghai,26-29, August 2004: 1109-1113.
[119]Asanga ratnawera, Saman K.Halgamuge, Self-organizing hierarchical partical swarm optimizer with time-varying acceleration coefficients [A].IEEE transactions on evolutionary computation [C].June 2004, 8(3): 240-256.
[120]Suganthan, P.N, Particle swarm optimizer with neighbourhood operator [A].Evolutionary computation [C].1999, CEC99, 6-9 July 1999, 3: 1958-1962.
[121]Clerc.M, The particle swarm-explosion, stability, and convergence in a multi-dimensional complex space [A].Evolutionary computation, IEEE transactions on [C].Feb.2002, 6(1): 58-73.
[122]Azuma Ide, Keiichiro yasuda.A basic study of the adaptive particle swarm optimization [A].IEEJ transition [C].2004, 124(2): 550-557.
[123]Pulido GT., Coello, C.A.C.A constraint-handling mechanism for particle swarm optimization [A].Evolutionary computation [C].2004.CEC2004.19-23 June 2004: 456-462.
[124]Lei Kaiyou, Qiu Yuhui.A study of constrained layout optimization using adaptive particle swarm optimizer [J].Journal of computer research and development, 2006,10: 33-40.
[125]Karin Zielinski, Rainer Laur.Constrained single-objective optimization using particle swarm optimization [A].IEEE Congress on evolutionary computation [C].July 16-21,2006: 101-109.
[126]Parsopoulos K E, Vrahatis M N.Particle swarm optimization method for constrained optimization problems[A].Proceedings of the 2002 Euro-International Symposium on Computation Intelligence [C].2002: 214-220.
[127]Graham Kendall, Yan Su.A particle swarm optimization approach in the construction of optimal risky portfolios [A].Proceedings of the 23~(rd) IASTED international multi-conference artificial intelligence and applications [C].Innsbruck, Austria Feb 14-16, 2005:1235-1241.
[128]Wang Yong, Cai Zixing, Zhen Wei, Liu Hui.A new evolutionary algorithm for solving constrained optimization problems [J].Journal of central south university (science and technology), 2006, 37(1):119-123.
[129]Tetsuyuki Takahama, Setsuko Sakai.Constrained optimization by a constrained particle swarm optimizer [J].Journal of advanced computational intelligence and intelligent informatics, 2005, 9(3):282-289.
[130]Homaiffar A., Qi C, Lai S.Constrained optimization via genetic algorithms [J].Simulation, 1994, 62(4): 242-254.
[131]Joines, J, Houck, C.On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's [A].Proceedings of first IEEE conference on evolutionary computation [C].1994: 579-584.
[132]Powell, D., Skoinick, M.Using genetic algorithms in engineering design optimization with non-linear constraints [A].Proceedings of the fifth international conference on genetic algorithms [C].1993:270-271.
[133]F.van den Bergh.An analysis of particle swarm optimizers [D].University of Pretoria, Doctorial Dissertation.2001.
[134]Forrest S, Perelson A S, Allen L, et al.Self non-self discrimination in a computer [A].In Proceedings of the 1994 IEEE Symposium on Research in Security and Privacy[C].Los Alamos CA IEEE Computer Society Press, 1994.
[135]D'haeseller P, Forrest S.An immunological approach to change detection: Algorithm, analysis and implication [A].In: Proc.of the IEEE Symp.On Research in Security and Privacy [C].Oakland:IEEE Computer Society Press, 1996:110-119.
[136]Fabio Gonzalez, Dipankar Dasgupta, Luis Fernando Nifio.A randomized real-valued negative selection algorithm [A].In Proceedings of the 2~(nd) International Conference on Artificial Immune Systems [C].Edinburgh, UK, September 2003: 261-272.
[137]Ji Z, Dasgupta D.Real-valued negative selection algorithm with variable-seized detectors[A].Genetic and Evolutionary Computation [C].Seattle: IEEE Press, 2004: 287-298.
[138]Wenjian Luo, Xin Wang, Ying Tan, Xufa Wang.A novel negative selection algorithm with an array of partial matching lengths for each detector [A].PPSN IX [C].LNCS 4193, 2006: 112-121.
[139]Mohammad Reza Ahmadi, Davood Maleki.A co-evolutionary immune system framework in a grid environment for enterprise network security [A].SSI 2006 [C].November 9th, 2006: 1136-1143.
[140]Jungan Chen, Feng Liang, Dongyong Yang.Dynamic negative selection algorithm based on match range model [A].LNCS [C].2005, 38(9): 1201-1208.
[141]Y.J.Cao, Q.H.Wu.Convergence analysis of adaptive genetic algorithms, Genetic algorithm in engineering systems: Innovations and applications [A].2-4 September 1997, Conference publication No.446, IEEE [C].1997: 2301-2309.
[142]Katja L, Rainer B, Tansu A, Achim M, Sahin A.A cooperative AIS framework for intrusion detection [A].IEEE Comminication society subject matter experts for publication in ICC 2007 proceedings [C].2007: 1409-1416.
[143]Shen He, Wenjian Luo, Xifa Wang.A negative selection algorithm with the variable length detector [J].Journal of Software, Vol.18, No.6, June 2007: 1361-1368.
[144]Jungwon Kim, Peter J.Bentley.An evaluation of negative selection in an artificial immune system for network intrusion detection [A].Proceedings of the Genetic and Evolutionary Conference (GECCO-2001)[C].2001: 1330-1337.
[145]Xiaoli Xiao, Yuehong Tian, Chuan Chen.Anomaly detection based on improved negative selection matching algorithm [J].Computer Applications, Feb, 2005, 25(2): 383-385.
[146]Zhou Ji, Dipankar Dasgupta.Real-valued negative selection algorithm with variable-sized detectors [A].Genetic and Evolutionary Computation [C].Washington: IEEE Press, 2005: 88-97.
[147]Kim J, Bentley P.The human immune system and network intrusion detection [A].7~(th) European Congress on Intelligent Techniques and Soft Computing (EURIT' 99) [C].1999:1909-1919.
[148]Luther K, Bye R, Alpcan T, Muller A.A cooperative AIS framework for intrusion detection [A].ICC 07, IEEE International Conference on Communications [C].24-28 June 2007:1409-1416.
[149]Jungwon Kim, Peter J.Bentley, Uwe A, Julie G Immune system approaches to intrusion detection-a review[A].In: ICARIS-2004, 3~(rd) International Conference on Artificial Immune Systems [C].LNCS 3239,2004:316-329.
[150]Wenjian Luo, Xianbin Cao, Xufa Wang.Research on adaptively generating detector algorithm [J].Acta Automatica Sinica, 2005.11, 31(6): 907-916.
[151]Tao Li.An immune based dynamic intrusion detection model [J].Chinese Science Bulletin, 2005 (22): 2650-2657.
[152]Tao Li.Computer Immunology [M].Publishing house of electronics industry, 2004.7.
[153]Jungan Chen.Intrusion detector maturation algorithm based on artificial immune system [D].Zhejiang: Zhejiang University of Technology, 2004, pp.43-48.
[154]Roberto, Di Pietro, Luigi V.Mancini.Intrusion detection system [A].Springer-Verlag New York Inc.[C].August 1st, 2008: 2301-2317.
[155]J.Redish.Expanding usability testing to evaluate complex system [J].Journal of usability studies,2007 2(3): 102-111.
[156]R.Werlinger, K.Hawkey, K.Benznosov.Security practitioners in context: their activities are iterations [A].In CHI'08 extended abstracts on human factors in computing system [C].2008:3789-3794.
[157]Dong Seong Kim, Jong Sou Park.Modeling network intrusion detection system using feature selection and parameters optimization [A].IEICE transactions on information and systems [C].2008 E91-D(4): 1050-1057.
[158]A.Gagne, K.Muldner, K.Beznosov.Identifying differences between security and other IT professionals: a qualitative analysis [A].In proc.of human aspects of information security and assurance (HAISA) [C].Plymouth, England, July 2008: 1201-1209.
[159]Hunt, J.Timmis, D.Cooke, M.Neal, C, King.The development of an artificial immune system for real world application.Application of artificial immune systems [A].D.Dasgupta Ed, Pub.Springer-Verlag [C].ISBN 3-540-64390-7, 1999: 157-186.
[160]J.Twycross, U.Aickelin.Libtissue-implementing innate immunity [A].In the preoceedings of IEEE world congress on computational intelligence [C].Vancourver, Canada, July 17-21, 2006: 909-907.
[161]Zhang Ran.Research on key technology of dynamic adaptive intrusion detection [D].Xian Jiaotong University, Doctorial Dissertation.2003.
[162]Balazinska M, Merlo E, Dagenais M.Advanced clone-analysis to support object-oriented system refactoring [A].The 7 working conference on reverse engineering [C].Brisbane, Australia, 2000:709-713.
[163]Northcutt S, Novak J.Network intrusion detection: an analysis handbook [M].New Riders Press,2000.
[164]Chen U, Cheng XQ, Li Y, Dai L.Lightweight intrusion detection system based on feature selection [J].Journal of software, 2007, 18(7): 1639-1651.
[165]Qusay Mahmoud, Cognitive network: Towards self-aware networks [M].Wiley publisher, September 24th, 2007.
[166]James Dougherty, Ron Kohavi, Mehran Sahami.Supervised and unsupervised descretization of continuous features [A].Machine learning: Proceedings of the twelfth international conference [C].1995: 1209-1213.
[167]UM Fayyad, K B Irani.Multi-interval discretization of continuous-valued attributes for classification learning [A].In proceedings of the 13th internaitonl joint conference on artificial intelligence [C].1993:423-410.
[168]Dataset [DB]: http://www.ailab.si/orange/datasets.asp.2008.3.
[169]David Nguyen, Abhishek Das, Gokhan Memik, Alok Choudhary.A reconfigurable architecture for network intrusion detection using principal component analysis [A].Proceedings of the 2006 ACM/SIGDA 14~(th) international symposium on field programmable gate arrays [C].Feb.2006: 235-236.
[170]Jonathon Shlens.A tutorial on principal component analysis [R].Systems neurobiology laboratory,University of California, San Diego, December 10, 2005.
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