改进的遗传算法及其在工程优化中的应用
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摘要
进化计算,作为一种新兴的强大的智能优化技术,已经广泛地应用在工程科学的几乎所有的领域。与传统优化方法相比,进化计算在全局优化、复杂设计区域、复杂目标函数及易用性等方面都显示出了其优越性。遗传算法是进化计算中最重要的算法之一,本文主要研究了改进的遗传算法及其在工程优化领域中的应用。
本文的主要研究内容分章介绍如下:
第一章首先简要介绍了进化计算的基本知识,在工程优化领域的历史与研究现状。本章的最后给出了本论文的基本框架。
第二章介绍了遗传算法的基本理论与方法。首先给出了遗传算法的基本流程,然后介绍了染色体的编码方法及遗传算子,最后分析了遗传算法的搜索机理与收敛性。
第三章提出了一种新的遗传算法——基于子域搜索的遗传算法(简称为SBGA)。SBGA将设计区域分成多个小的子域,根据已搜索过的样本点在这些子域内的分布情况来指导后续的搜索。同时,SBGA还提供了一种新的处理约束的方法。应用SBGA进行了复杂函数和杆系结构的优化,数值实验还发现它能够有效地抑制早熟现象的发生。
第四章研究了基于遗传算法的连续体结构拓扑优化问题。提出了一种新的变长的紧凑编码方式——链码编码方法,用四方向链码的组合来描述结构拓扑。以机器学习中的范例推理为原型,设计出一种基于范例推理的遗传算法。在该算法中,根据多尺度变换的原理,提出了结构拓扑的“目标向量”描述方式,从而可能定量地描述不同拓扑的相似性。研制了上述方法的计算机软件GATOCS,并利用该软件对多工况连续深梁和自行车框架进行拓扑优化,取得了较为满意的结果,从而说明了应用遗传算法进行连续体结构拓扑优化是完全可行的。
第五章研究了多目标遗传算法及其并行实现。实际的工程优化问题通常都是多目标的,而且计算量很大。因此,本文研究了并行虚拟机(高速互联机
Evolutionary computation (EC), as a novel and powerful intelligent optimization technology, has been utilized extensively in almost every branch in engineering science. It has more advantages over traditional optimization methods when solving problems with global search, complex design domain and complicated target functions, and it is easier to use. Genetic algorithm (GA) is one of the most important algorithms in evolutionary computation. An extensive study of improved genetic algorithms in the context of engineering optimization design has been conducted in this dissertation.The dissertation is organized as following chapters.First, a general introduction to genetic algorithms is presented in chapter 1, and the past and recent developments in this field are briefly described. The framework of the dissertation is also figured out in the end of chapter 1.Next, theoretical aspects and implementation of genetic algorithms are focused on in chapter 2. The basic workflow of genetic algorithms is described at the beginning of this chapter, and then typical representation and genetic operators are discussed. Finally, the search mechanism and convergence are investigated in the end of the chapter.Next, a novel genetic algorithm called subdomain based genetic algorithms (SBGA) is proposed in chapter 3. In SBGA, design space is divided into many small and isolate subdomains, and distribution information in these subdomains will be traced in the process of evolution, which will be used to guide subsequent search. At the same time, SBGA also provides a new method to handle complicated constraints. The numerical results demonstrate that SBGA is effective and efficient to alleviate premature convergence.Next, GA based topology optimization methods of continuum structures are studied in chapter 4. A new compact representation called four direction chain code representation is proposed in this chapter, and case-based genetic algorithms enlightened by machine learning are developed, in which a new concept named "Target Vector" is utilized to calculate the distance between two structures with
different topology. The expected results are obtained when applying above approach to continuum beam and bike framework topology optimization.Next, practical engineering optimum problems are of more than one target functions and computational cost is very high, so multiobjective optimization methods are discussed in chapter 5. To improve computing performance, a parallel version of improved strength evolutionary algorithms (SPEA2) is presented to optimize beam topology with two conflicting targets on the environment of cluster of workstations connected with each other.Next, a hybrid genetic algorithm is proposed which combine niche and local search techniques in chapter 6. "Mutual Information" is introduced to calculate the matching degree of two medical images from different modalities and very good results are obtained.Finally, a summary of the research conclusions, a list of innovation points and a discussion on the most promising paths of future research are also presented in chapter 7.
本文的主要研究内容分章介绍如下:
第一章首先简要介绍了进化计算的基本知识,在工程优化领域的历史与研究现状。本章的最后给出了本论文的基本框架。
第二章介绍了遗传算法的基本理论与方法。首先给出了遗传算法的基本流程,然后介绍了染色体的编码方法及遗传算子,最后分析了遗传算法的搜索机理与收敛性。
第三章提出了一种新的遗传算法——基于子域搜索的遗传算法(简称为SBGA)。SBGA将设计区域分成多个小的子域,根据已搜索过的样本点在这些子域内的分布情况来指导后续的搜索。同时,SBGA还提供了一种新的处理约束的方法。应用SBGA进行了复杂函数和杆系结构的优化,数值实验还发现它能够有效地抑制早熟现象的发生。
第四章研究了基于遗传算法的连续体结构拓扑优化问题。提出了一种新的变长的紧凑编码方式——链码编码方法,用四方向链码的组合来描述结构拓扑。以机器学习中的范例推理为原型,设计出一种基于范例推理的遗传算法。在该算法中,根据多尺度变换的原理,提出了结构拓扑的“目标向量”描述方式,从而可能定量地描述不同拓扑的相似性。研制了上述方法的计算机软件GATOCS,并利用该软件对多工况连续深梁和自行车框架进行拓扑优化,取得了较为满意的结果,从而说明了应用遗传算法进行连续体结构拓扑优化是完全可行的。
第五章研究了多目标遗传算法及其并行实现。实际的工程优化问题通常都是多目标的,而且计算量很大。因此,本文研究了并行虚拟机(高速互联机
Evolutionary computation (EC), as a novel and powerful intelligent optimization technology, has been utilized extensively in almost every branch in engineering science. It has more advantages over traditional optimization methods when solving problems with global search, complex design domain and complicated target functions, and it is easier to use. Genetic algorithm (GA) is one of the most important algorithms in evolutionary computation. An extensive study of improved genetic algorithms in the context of engineering optimization design has been conducted in this dissertation.The dissertation is organized as following chapters.First, a general introduction to genetic algorithms is presented in chapter 1, and the past and recent developments in this field are briefly described. The framework of the dissertation is also figured out in the end of chapter 1.Next, theoretical aspects and implementation of genetic algorithms are focused on in chapter 2. The basic workflow of genetic algorithms is described at the beginning of this chapter, and then typical representation and genetic operators are discussed. Finally, the search mechanism and convergence are investigated in the end of the chapter.Next, a novel genetic algorithm called subdomain based genetic algorithms (SBGA) is proposed in chapter 3. In SBGA, design space is divided into many small and isolate subdomains, and distribution information in these subdomains will be traced in the process of evolution, which will be used to guide subsequent search. At the same time, SBGA also provides a new method to handle complicated constraints. The numerical results demonstrate that SBGA is effective and efficient to alleviate premature convergence.Next, GA based topology optimization methods of continuum structures are studied in chapter 4. A new compact representation called four direction chain code representation is proposed in this chapter, and case-based genetic algorithms enlightened by machine learning are developed, in which a new concept named "Target Vector" is utilized to calculate the distance between two structures with
different topology. The expected results are obtained when applying above approach to continuum beam and bike framework topology optimization.Next, practical engineering optimum problems are of more than one target functions and computational cost is very high, so multiobjective optimization methods are discussed in chapter 5. To improve computing performance, a parallel version of improved strength evolutionary algorithms (SPEA2) is presented to optimize beam topology with two conflicting targets on the environment of cluster of workstations connected with each other.Next, a hybrid genetic algorithm is proposed which combine niche and local search techniques in chapter 6. "Mutual Information" is introduced to calculate the matching degree of two medical images from different modalities and very good results are obtained.Finally, a summary of the research conclusions, a list of innovation points and a discussion on the most promising paths of future research are also presented in chapter 7.
引文
[1] 遗传学与物种起源。谈家桢等译。北京:科学出版社,1964年8月,1982年11月重印。
[2] 遗传学编写组。遗传学。北京:中国大百科全书出版社,1983。
[3] Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, Michigan: University of Michigan Press.
[4] Whitley, L. D. (1989). The GENITOR algorithm and selection pressure: why ranked-based allocation of reproductive trials is best. In J. D. Schaffer (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA'89), Fairfax, VA, USA, 239-C255.
[5] Yang, J., & Soh, C. K. (1997). Structural optimization by genetic algorithms with tournament selection. Journal of Computing in Civil Engineering, 11(3), 195-200.
[6] Holland, J.H. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. First edition, Ann Arbor, MI: the university of Michigan Press, 1975;2nd edition, Cambridge,MA:MIT press, 1992.
[7] 徐宗本,计算智能(第一册)——模拟进化计算。北京:高等教育出版社。2004年2月第一版。
[8] 李敏强,寇纪淞等,遗传算法的基本理论与应用。北京:科学出版社。2002.3第一版。
[9] Goldberg, D.E., and Segrest, P. Finite Markov chain analysis of genetic algorithm. In proceedings of the second international conference on genetic algorithm(ICGA 2), 1987: 1～8
[10] Eiben, A. E., Arts, E. H., and Van Hee, K. M. Global convergence of genetic algorithm: an Infinite Markov chain analysis. In Parallel Problem Solving from Nature(PPSN 1), 1991:4～12
[11] Rudolph, G. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 1994,5(1):86～101
[12] Whitley, L. D. Foundations of genetic algorithms 2. San Mateo, CA: Morgan Kaufmann, 1993
[13] Rabinovich Y and Wigderson A. An analysis of a simple genetic algorithm. In proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, Morgan Kaufmann, 1991:215～221
[14] Chakraborty U K and Dastidar D G. Using reliability analysis to estimate the number of generations to convergence in genetic algorithms. Information Processing Letters, 1993,46:199-209
[15] Salomon R. Raising theoretical questions about the utility of genetic algorithms. In evolutionary programming 4, Lecutre Notes in Computer Science 1213,1997:275~284
[16] Jenkins, W. M. Structural optimization with the genetic algorithm. Structural Engineer, 1991,69(24):408~422
[17] Forrest, S. Genetic algorithm: principles of natural selection application to computation. Science, Aug,1993,261:872-878
[18] Xin Yao. A review of evolutionary artificial neural network. International Journal of Intelligent Systems, 1993,8(3):539~565
[19] Freeman, L. M., et al. Truning fuzzy logic controller using genetic algorithms in aerospace applications. In Proceeding of the AAAIC'90 Conference, Dayton, Oct., 1990:351-358
[20] Glover, F., et al. Genetic algorithms and tabu search: hybrids for optimization. Computer and Operation Research(UK), 1995, 122(1):111-134
[21] Davis, L. Adapting operator probabilities in genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms(ICGA 3), Schaffer, J. D. (ed.), San Mateo, CA: Morgan Kaufmann Publishers,1989:61~69
[22] Lobo, F. G. , and Goldberg, D. E. Decision making in a hybrid genetic algorithm. Proceedings of the 1997 IEEE Conference on Evolutionary Computation, New York: IEEE Press, 1997:121-125
[23] Belew, R. K., and Vose, M. D. Foundations of genetic algorithms 4. CA: Morgan Kaufmann Publishers, Inc., 1997
[24] Mahfoud, S. W. Crowding and pre-selection revisited. In Parallel Problem Solving from Nature(PPSN 2), Manner, R. , and Manderick, B.(eds.), North-Holland, Elsevier Science Publishers, Amsterdam, 1992
[25] Mengshoel, O. J., and Goldberg, D. E. Probabilistic crowding: deterministic crowding with probabilistic replacement. In Proceedings of the Genetic and Evolutionary Computation Conference 1999(GECCO-99), Banzhaf, W., et al(eds.) San Fransisco, CA: Morgan Kaufmann. 1999
[26] Goldberg, D. E., and Wang, L. Adaptive niching via co-evolutionary sharing. In Genetic Algorithms in Engineering and Computer Science, Quagliarella, et al.(eds.). John Wiley&Sons, Ltd. 1997:21-38
[27] Schmit L A., Structural optimization- some ideas and insights in: Atrek E, et al (eds). New direction in optimum design, New York: wiley,1984
[28] Vanderplaats G N., Numerical methods for shape optimization: an assessment of the state of the art, in: Atrek E, et al (eds). New direction in optimum design, New York: wiley,1984
[29] Spears, W. M. (2000). Evolutionary algorithms: the role of mutation and recombination. Berlin;New York: Springer.
[30] Kicinger, R., Arciszewski, T., and De Jong, K. A. (2005). "Evolutionary computation and structural design: a survey of the state of the art." Computers & Structures, 83(23-24), 1943-1978.
[31] Schmit, L. A. (1981). Structural synthesis- its genesis and development. AAAI Journal, 19(10), 1249-1263.
[32] Berke, L., & Khot, N. S. (1987). Structural optimization using optimality criteria. In C. A. Mota Soares (Ed.), Computer Aided Optimal Design: Structural and Mechanical System. Berlin: Springer-Verlag, 235-269.
[33] Anagnostou, G., Ronquist, E., & Patera, A. (1992). A computational procedure for part design. Computer Methods in Applied Mechanics and Engineering, 97,33-48.
[34] Kane, C, & Schoenauer, M. (1996). Topological optimum design using genetic algorithms. Control and Cybernetics, 25(5), 1059-1088.
[35] Anagnostou, G., Ronquist, E., & Patera, A. (1992). A computational procedure for part design. Computer Methods in Applied Mechanics and Engineering, 97, 33-48.
[36] Bendsoe, M. P., & Kikuchi, N. (1988). Generating optimal topologies in structural design using a homogenization method. Computer Methods in Applied Mechanics and Engineering,71, 197-224.
[37] Xie, Y. M., & Steven, G. P. (1992). Shape and layout optimization via an evolutionary procedure. In Proceedings of the International Conference on Computational Engineering Science, Hong Kong University of Science and Technology, Hong Kong.
[38] Sandgren, E., Jensen, E. D., & Welton, J. (1990). Topological design of structural components using genetic optimization methods. In Sensitivity Analysis and Optimization with Numerical Methods, AMD-vol. 115, Proceedings of the Winter Annual Meeting of the American Society of Mechanical Engineers. Dallas, TX, 31-43.
[39] Chapman, C. D., Saitou, K., & Jakiela, M. J. (1994). Genetic algorithm as an approach to configuration and topology design. Journal of Mechanical Design, 116,1005-1012.
[40] Jensen, E. D. (1992). Topological structural design using genetic algorithms. Ph.D.Dissertation Purdue University, Lafayette, IN.
[41] Periaux, J., & Winter, G. (Eds.). (1995). Genetic algorithms in engineering and computer science. Chichester, UK: John Wiley.
[42] Schoenauer, M. (1996). Shape representations and evolution schemes. In L. J. Fogel, P. J. Angeline & T. Back (Eds.), Proceedings of the Fifth Annual Conference on Evolutionary Programming, San Diego, CA, USA.
[43] Hamda, H., Jouve, F., Lutton, E., Schoenauer, M., & Sebag, M. (2002). Compact unstructured representations for evolutionary topological optimum design. Applied Intelligence, 16, 139-155
[44] Hamda, H., Roudenko, O., & Schoenauer, M. (2002). Multi-objective evolutionary topological optimum design. In I. C. Parmee (Ed.), Proceedings of the Fifth International Conference on Adaptive Computing Design and Manufacture (ACDM 2002), University of Exeter, Devon, UK, 121-132.
[45] Dorn, W. C, Gomory, R. E., & Greenberg, H. J. (1964). Automatic design of optimal structures. Journal de Mecanique, 3,25-52
[46] Shankar, N., & Hajela, P. (1991). Heuristics driven strategies for near-optimal structural topology development. In B. H. V. Topping (Ed.), Artificial Intelligence and Structural Engineering. Oxford, UK: Civil-Comp Press, 219-226.
[47] Hajela, P., Lee, E., & Lin, C.-Y. (1993). Genetic algorithms in structural topology optimization. In M. P. Bendsoe & C. A. Mota Soares (Eds.), Topology Design of Structures,117-133.
[48] Koumousis, V. K., & Georgiou, P. G. (1994). Genetic algorithms in discrete optimization of steel truss roofs. Journal of Computing in Civil Engineering, 8(3), 309-325.
[49] Bohnenberger, O., Hesser, J., & Manner, R. (1995). Automatic design of truss structures using evolutionary algorithms. In Proceedings of the Second IEEE International Conference on Evolutionary Computation (ICEC'95), Perth, Australia, 143-149.
[50] Rajan, S. D. (1995). Sizing, shape, and topology design optimization of trusses using genetic algorithm. Journal of Structural Engineering, 121,1480-1487.
[51] Rajeev, S., & Krishnamoorthy, C. S. (1997). Genetic algorithms-based methodologies for design optimization of trusses. Journal of Structural Engineering, 123(3), 350-358.
[52] Azid, I. A., Kwan, A. S. K., & Seetharamm, K. N. (2002). An evolutionary approach for layout optimization of a three-dimensional truss. Structural and Multidisciplinary Optimization, 24(4), 333-337.
[53] Bartholomew P, Morris A J, Stars-software package for structure optimization., in: Atrek E, et al (eds). New direction in optimum design, John Wiley & Sons Ltd, 1984:617～632
[54] 隋允康,离散结构断面优化方法和程序系统DDDU-4.上海力学,1987,8(4):12～23
[55] Pham D.T. and Yang Y., Optimization of multi-modal discrete functions using genetic algorithms. In Institute of Mechanical Engineering (part D), 1993, 53～59
[56] Lin C. Y., and Hajela P., EVOLVE: a genetic search based optimization code via multiple strategies, In Hern andez S and Brebbia C. A., editors, Computer Aided optimum Design of Sructures Ⅲ, Optimization of Structural System and Applications, Elsevier Applied Science, 1993,639～654
[57] Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191, 1245-1287.
[58] Michalewicz, Z. (1995). A survey of constraint handling techniques in evolutionary computation methods. In Proceedings of the 4th Annual Conference on Evolutionary Programming, Cambridge, MA, 135-155.
[59] Courant, R. (1943). Variational methods for the solution of problems of equilibrium and vibrations. Bulletin of American Mathematical Society, 49, 1-23. [100] Caroll, C. W. (1961). The created response surface technique for optimizing nonlinear restrained systems. Operations Research, 9, 169-184.
[60] Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, Mass.: Addison-Wesley Pub. Co.
[61] Richardson, J. T., Palmer, M. R., Liepins, G. E., & Hilliard, M. R. (1989). Some guidelines for genetic algorithms with penalty functions. In J. D. Schaffer (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA'89), Faiffax, VA, USA, 191-197.
[62] Goldberg, D. E., & Samtani, M. (1986). Engineering optimization via genetic algorithm. In Proceedings of the Ninth Conference on Electronic Computation, University of Alabama, Birmingham, 471-482.
[63] Joines, J. A., & Houck, C. R. (1994). On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. In Z. Michalewicz, J. D. Schaffer, H.-P. Schwefel, D. B. Fogel & H. Kitano (Eds.), Proceedings of the First IEEE International Conference on Evolutionary Computation (ICEC'94), Orlando, FL, USA, 579-584.
[64] Michalewicz, Z., & Attia, N. F. (1994). Evolutionary optimization of constrained problems. In A. V. Sebald & L. J. Fogel (Eds.), Proceedings of the Third Annual Conference on Evolutionary Programming, San Diego, CA, USA, 98-108..
[65] Bean, J. C, & Hadj-Alouane, A. B. (1992). A dual genetic algorithm for bounded integer programs (Technical Report No. TR 92-53): Department of Industrial and Operations Engineering, University of Michingan.
[66] Coello Coello, C. A. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113-127.
[67] Schwefel, H.-P. (1981). Numerical optimization of computer models. Chichester, UK: John Wiley & Sons.
[68] Bean, J. C. (1994). Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6,154-160.
[69] Goldberg D. E. and Samtani M.P. Engineering optimization via genetic algorithm, In 9th Conference on Electronic computation, New York, N. Y., ASCE, 1986
[70] Koziel, S., & Michalewicz, Z. (1999). Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evolutionary Computation, 7(1), 19-44.
[71] Michalewicz, Z. (2000). Repair algorithms. In T. Back, D. B. Fogel & Z. Michalewicz (Eds.), Evolutionary computation 2: advanced algorithms and operators (Vol. 2). Bristol and Philadelphia: Institute of Physics Publishing, 56-61.
[72] Kicinger, R., Arciszewski, T., & De Jong, K. A. (2003). Evolutionary designing of steel structures in tall buildings. Journal of Computing in Civil Engineering(tentatively approved).
[73] Paredis, J. (1994). Co-evolutionary constraints satisfaction. In Y. Davidor, H.-P. Schwefel & R. Manner (Eds.), Proceedings of the Third International Conference on Parallel Problem Solving from Nature (PPSN-Ⅲ), Jerusalem, Israel, 46-55
[74] Surry, P. D., Radcliffe, N. J., & Boyd, I. D. (1995). A multi-objective approach to constrained optimization of gas supply networks: the COMOGA method. In T. C. Fogarty (Ed.), Proceedings of the AISB-95 Workshop on Evolutionary Computing, Sheffield, UK,166-180.
[75] Le, T. V. (1995). A fuzzy evolutionary approach to constrained optimization problems. In Proceedings of the Second IEEE International Conference on Evolutionary Computation (ICEC'95), Perth, Australia, 274-278.
[76] Forrest, S., & Perelson, A. S. (1990). Genetic algorithms and the immune system. In H.-P. Schwefel & R. Manner (Eds.), Proceedings of the First International Conference on Parallel Problem Solving from Nature (PPSN-I), Dortmund, Germany, 320-325
[77] Chankong, V., & Haimes, Y. Y. (1983). Multiobjective decision making: theory and methodology. New York: North Holland.
[78] Schaffer, J. D. (1985). Multiple objective optimization with vector evaluated genetic algorithms
[79] Cvetkovic, D., Parmee, I. C, & Webb, E. (1998). Multi-objective optimisation and preliminary airframe design. In I. C. Parmee (Ed.), The Integration of Evolutionary and Adaptive Computing Technologies with Product/System Design and Realisation. Plymouth, UK: Springer-Verlag, 255-267.
[80] Coello Coello, C. A., & Christiansen, A. D. (2000). Multiobjective optimization of trusses using genetic algorithms. Computers & Structures, 75(6), 647-660
[81] Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multi-objective optimization: formulation, discussion, and generalization. In S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA'93), Urbana-Champaign, IL, USA, 416-423.
[82] Chipperfield, A. J., & Fleming, P. J. (1995). Gas turbine engine controller design using multiobjective genetic algorithms. In A. M. S. Zalzala (Ed.), Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA'95), Halifax Hall, University of Sheffield, UK.
[83] Obayashi, S. (2002). Pareto solutions of multipoint design of supersonic wings using volutionary algorithms. In I. C. Parmee (Ed.), Adaptive Computing in Design and Manufacture V. London: Springer-Verlag, 3-16.
[84] Grierson, D. E., & Khajehpour, S. (2002). Method for conceptual design applied to office buildings. Journal of Computing in Civil Engineering, 16(2), 83-103.
[85] Srinivas, N., & Deb, K. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221-248.
[86] Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). Fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo Guervos & H.-P. Schwefel (Eds.), Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN-Ⅵ), Paris, France, 849-858.
[87] Hamda, H., Roudenko, O., & Schoenauer, M. (2002). Multi-objective evolutionary topological optimum design. In I. C. Parmee (Ed.), Proceedings of the Fifth International Conference on Adaptive Computing Design and Manufacture (ACDM 2002), University of Exeter, Devon, UK, 121-132
[88] Horn, J., & Nafpliotis, N. (1993). Multiobjective optimization using the niched Pareto genetic algorithm (Technical Report No. IlliGAI Report 93005). Urbana, Illinois, USA: University of Illinois at Urbana-Champaign.
[89] Van Veldhuizen, D. A., & Lamont, G. B. (1998). Multi-onjective evolutionary algorithm research: a history and analysis (No. TR-98-03). Wright-Patterson AFB, Ohio: Department of Electrical and Computer Engineering, Air Force Institute of Technology.
[90] 玄光男,程润伟。遗传算法与工程优化。北京:清华大学出版社,2003
[91] Michalewicz, Z. (1996). Genetic algorithms+data structures=evolution programs (3rd rev. and extended ed.). Berlin;New York: Springer-Verlag.
[92] 徐宗本,高勇。遗传算法过早收敛现象的特征分析及其预防。中国科学E辑,1996,26(4),364—375
[93] 张文修,梁怡。遗传算法的数学基础。西安:西安交通大学出版社,2000
[94] Whitley, D. The Genitor Algorithm and Selection Pressure: Why Rank-Based Allocation Reproduction Trials is Best, In: Schaffer, J. (Editor), Proceeding of the 3rd International Conference on Genetic Algorithm, Morgan Kaufmann Publishers, 1989
[95] Goldberg D E. Genetic Algorithms in Search Optimization and Machine Learning. Massachusetts: Addison-Wesley Press, 1989
[96] Herrera F, Lozano M. Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In: Herrera F, Verdegay JL, eds. Genetic Algorithms and Soft Computing. Physica-Verlag (Studies in Fuzziness and Soft Computing, Vol. 8), 1996. 95～125.
[97] Gantovnik, Vladimir B. and Anderson-Cook, Christine M. and Gurdal, Zafer and Watson, Layne T, A genetic algorithm with memory for mixed discrete-continuous design optimization。Technical Report TR-03-12, Computer Science, Virginia Tech, 2003
[98] Whitley, D. The GENITOR. Algorithm and Selection Pressure: Why Rank-Based Allocation Reproduction Trials is Best, In: Schaffer, J. (Editor), Proceeding of the 3rd International Conference on Genetic Algorithm, Morgan Kaufmann Publishers,1989
[99] Goldberg D E. Genetic Algorithms in Search Optimization and Machine Learning. Massachusetts: Addison-Wesley Press, 1989
[100] 苏小红,杨博,王亚东。基于进化稳定策略的遗传算法。软件学报,2003,14(1):1863-1868
[101] Herrera F, Lozano M. Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In: Herrera F, Verdegay JL, eds. Genetic Algorithms and Soft Computing. Physica-Verlag (Studies in Fuzziness and Soft Computing, Vol. 8), 1996. 95～125.
[102] Caroline Ravise and Michele Sebag_ An advanced evolution should not repeat its past errors. In Thirteenth International Conference on Machine Learning, 1996
[103] 边肇祺,张学工编著:《模式识别》,北京:清华大学出版社,2000
[104] A. Collignon, Multi-modality medical image registration by maximization of mutual information, Ph.D. thesis, Catholic University of Leuven, Leuven, Belgium, 1998.
[105] B. Fei, A. Wheaton, Z. Lee, J. L. Duerk, and D. L. Wilson, "Automatic MR volume registration and its evaluation for the pelvis and prostate," Physics in Medicine and Biology, vol. 47, no. 5, pp. 823-838, 2002.
[106] P. J. Slomka, J. Mandel, D. Downey, and A. Fenster, "Evaluation of voxel-based registration of 3-D power Doppler ultrasound and 3-D magnetic resonance angiographic images of carotid arteries," Ultrasound in Medicine and Biology, vol. 27,no. 7, pp. 945-955, 2001.
[107] P. E. Radau, P. J. Slomka, P. Julin, L. Svensson, and L.-O. Wahlund, "Evaluation of linear registration algorithms for brain SPECT and the errors due to hypoperfusion lesions," Medical Physics, vol. 28, no. 8, pp. 1660-1668, 2001.
[108] W.M. Wells Ⅲ, P.Viola, et al. Multimodal volume registration by maximization of mutual information.Medical Image Analysis, 1996,1(1): 35～51.
[109] 杨虎,马斌荣等,基于互信息的人脑图像配准研究,中国医学物理学杂志,2001,18,69～73
[110] C. Studholme, D. L. G. Hill, and D. J. Hawkes, "An overlap invariant entropy measure of 3D medical image alignment," Pattern Recognition, vol. 32, no. 1, pp. 71-86, 1999
[111] Maes F, Collignon A, Vandermeulen D, et al. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 1997,16(2): 187～198
[112] Spears, W. M. "Adapting Crossover in Evolutionary Algorithms". In Proceeding of the Fourth Annual Conference on Evolutionary Programming in San Diego, CA, 1991
[113] 李芳,凌道盛,工程结构优化设计发展综述。工程设计学报,2002,12 vol,9 NO.5,229～235
[114] Xie Y M, Steven G P. A simple evolutionary procedure for structural optimization[J]. Comput &Struct, 1993,49(5):885—896.
[115] 章毓晋,图像处理与分析。北京:清华大学出版社。1999年3月第一版。
[116] Couro Kane, Marc Schoenauer. Topological Optimum Design using Genetic Algorithms. Control and Cybernetics, 25(5):1059-1088, 1996.
[117] Van Veldhuizen,D.A.(1999). Multiobjective evolutionary algorithms: Classfication,Analysis, and new innovations. PH.D. thesis. AFIT/DS/ENG/99-01, Air force institute of technology, Wright Patterson AFB, Ohio.
[118] Kalyanmoy Deb and Gushar Goel. Multi-Objective Evolutionary Algorithms for Engineering Shape Design.
[119] Rosenberg, R.S. Simulation of genetic population with biochemical properties. Ph.D. Dissertation University of Michigan, Ann Harbor, Michigan, 1967
[120] Schaffer, J.D. Some experiments in machine learning using vector evaluated genetic algorithms. Ph.D. Dissertation Vanderbilt University,Nashville, TN, 1984
[121] Zitzler E,Thiele L. MultiObjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on evolutionary computation, 1999,3(4):257-271
[122] Zitzler E, Laumanns M, and Thiele L. SPEA2:Improving the Strength Pareto Evolutionary Algorithms.
[123] Silverman,B. Density estimation for statistics and data analysis. London: Chapman and Hall. 1986
[124] 陆鑫达等译。并行程序设计。北京:机械工业出版社,2002.
[125] 陈国良,安虹等。并行算法实践。北京:高等教育出版社,2004.
[126] 郭彤城,慕春棣。并行遗传算法的新进展。系统工程理论与实践,2002年第2期。
[127] ANSI Technical Computing X3H5. 1993, Parallel processing model for high-level programming language.
[128] Gustafson J L. 1988. Reevaluating Amdahl's Law. Comm. Of ACM, 31:532-533
[129] Sun X H and Ni L M. 1990. Another view of parallel speed. Proc. Supercomputing'90
[130] Cantu-Paz, E.(2000).Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers, Norwell,MA.
[131] Erick Cantu-Paz. A survey of Parallel Genetic Algorithms.
[132] GROSSO P. B.,Computer simulations of genetic adaptation : Parallel subcomponent subcomponent interaction in a multilocus model doctoral dissertation, The University of Michigan, 1985.
[133] PETTEY C. B., LEUZE M. R., GREFENSTETTE J. J., A parallel genetic algorithm . In GREFENSTETTE J. J., Ed., Proceedings of the Second International Conference。 on Genetic Algorithms, p. 155-161, Lawrence Erlbaum Associates (Hillsdale, NJ), 1987.
[134] MUHLENBEIN H., SCHOMISCHM. , BORN J., 《The Parallel Genetic Algorithm as Function Optimizer 》. In BELEW R. K., BOOKER L. B., Eds., Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann (San Mateo, CA), 1991.
[135] PETTEY C. C, LEUZE M. R., A theoretical investigation of a parallel genetic algorithm. In SCHAFFER J. D., Ed., Proceedings of the Third International Conference on Genetic Algorithms, p. 398-405, Morgan Kaufmann (San Mateo, CA), 1989.
[136] BRAUN H. C, On Solving Travelling salesman Problems by Genetic Algorithms. In SCHWEFEL H.-P., MANNER R., Eds., Parallel Problem Solving from Nature, p. 129-133, Springer-Verlag (Berlin), 1990.
[137] MUNETOMO M., TAKAI Y., SATO Y., An efficient migration scheme for subpopulation-based asynchronously parallel genetic algorithms. In FORREST S., Ed., Proceedings of the Fifth International Conference on Genetic Algorithms, page 649, Morgan Kaufmann (San Mateo,CA), 1993.
[138] MUNETOMO M., TAKAI Y., SATO Y.,An efficient migration scheme for subpopulation-based asynchronously parallel genetic algorithms. In FORREST S., Ed., Proceedings of the Fifth International Conference on Genetic Algorithms, page 649, Morgan Kaufmann (San Mateo, CA), 1993.
[139] MARIN F. J., TRELLES-SALAZAR O. , SANDOVAL F., Genetic algorithms on LAN- message passing architectures using PVM : Application to the routing problem. In DAVIDOR Y., SCHWEFEL H.-P., MANNER R., Eds., Parallel Problem Solving fron Nature, PPSN III, p. 534-543, Springer-Verlag (Berlin), 1994.
[140] BESSIERE P., TALBI E.-G..A parallel genetic algorithm for the graph partitioning problem. In ACM Int. Conf. on Supercomputing ICS91, Cologne, Germany, July 1991.
[141] SEREDYNSKI F., Dynamic mapping and load balancing with parallel genetic algorithms. In Proceedings of the First IEEE Conference on Evolutionary Computation, vol. 2, p. 834-839, IEEE Press (Piscataway, NJ), 1994.
[142] DAVIS M., LIU L., ELIAS J. G., VLSI circuit synthesis using a parallel genetic algorithm. In Proceedings of the First IEEE Conference on Evolutionary Computation, vol. 1, p. 104-109, IEEE Press (Piscataway, NJ), 1994.
[143] PunchWF,AverillRC,GoodmanED,etal.Optimal-design of laminated composite structures using coarse-grain parallel genetic algorithms [J]. Comput. Syst.Eng., 1994, 5(4-6):415-423.
[144] 杨堃,并行计算及其在模拟风沙跃移运动中的应用。兰州大学硕士学位论文,固体力学系,2004
[145] 马光文,王黎。遗传算法在桁架结构优化设计中的应用。工程力学,1998;15(2):38～44
[2] 遗传学编写组。遗传学。北京:中国大百科全书出版社,1983。
[3] Holland, J. H. (1975). Adaptation in natural and artificial systems. Ann Arbor, Michigan: University of Michigan Press.
[4] Whitley, L. D. (1989). The GENITOR algorithm and selection pressure: why ranked-based allocation of reproductive trials is best. In J. D. Schaffer (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA'89), Fairfax, VA, USA, 239-C255.
[5] Yang, J., & Soh, C. K. (1997). Structural optimization by genetic algorithms with tournament selection. Journal of Computing in Civil Engineering, 11(3), 195-200.
[6] Holland, J.H. Adaptation in natural and artificial systems: An introductory analysis with applications to biology, control, and artificial intelligence. First edition, Ann Arbor, MI: the university of Michigan Press, 1975;2nd edition, Cambridge,MA:MIT press, 1992.
[7] 徐宗本,计算智能(第一册)——模拟进化计算。北京:高等教育出版社。2004年2月第一版。
[8] 李敏强,寇纪淞等,遗传算法的基本理论与应用。北京:科学出版社。2002.3第一版。
[9] Goldberg, D.E., and Segrest, P. Finite Markov chain analysis of genetic algorithm. In proceedings of the second international conference on genetic algorithm(ICGA 2), 1987: 1～8
[10] Eiben, A. E., Arts, E. H., and Van Hee, K. M. Global convergence of genetic algorithm: an Infinite Markov chain analysis. In Parallel Problem Solving from Nature(PPSN 1), 1991:4～12
[11] Rudolph, G. Convergence analysis of canonical genetic algorithms. IEEE Transactions on Neural Networks, 1994,5(1):86～101
[12] Whitley, L. D. Foundations of genetic algorithms 2. San Mateo, CA: Morgan Kaufmann, 1993
[13] Rabinovich Y and Wigderson A. An analysis of a simple genetic algorithm. In proceedings of the Fourth International Conference on Genetic Algorithms, San Mateo, Morgan Kaufmann, 1991:215～221
[14] Chakraborty U K and Dastidar D G. Using reliability analysis to estimate the number of generations to convergence in genetic algorithms. Information Processing Letters, 1993,46:199-209
[15] Salomon R. Raising theoretical questions about the utility of genetic algorithms. In evolutionary programming 4, Lecutre Notes in Computer Science 1213,1997:275~284
[16] Jenkins, W. M. Structural optimization with the genetic algorithm. Structural Engineer, 1991,69(24):408~422
[17] Forrest, S. Genetic algorithm: principles of natural selection application to computation. Science, Aug,1993,261:872-878
[18] Xin Yao. A review of evolutionary artificial neural network. International Journal of Intelligent Systems, 1993,8(3):539~565
[19] Freeman, L. M., et al. Truning fuzzy logic controller using genetic algorithms in aerospace applications. In Proceeding of the AAAIC'90 Conference, Dayton, Oct., 1990:351-358
[20] Glover, F., et al. Genetic algorithms and tabu search: hybrids for optimization. Computer and Operation Research(UK), 1995, 122(1):111-134
[21] Davis, L. Adapting operator probabilities in genetic algorithms. In Proceedings of the Third International Conference on Genetic Algorithms(ICGA 3), Schaffer, J. D. (ed.), San Mateo, CA: Morgan Kaufmann Publishers,1989:61~69
[22] Lobo, F. G. , and Goldberg, D. E. Decision making in a hybrid genetic algorithm. Proceedings of the 1997 IEEE Conference on Evolutionary Computation, New York: IEEE Press, 1997:121-125
[23] Belew, R. K., and Vose, M. D. Foundations of genetic algorithms 4. CA: Morgan Kaufmann Publishers, Inc., 1997
[24] Mahfoud, S. W. Crowding and pre-selection revisited. In Parallel Problem Solving from Nature(PPSN 2), Manner, R. , and Manderick, B.(eds.), North-Holland, Elsevier Science Publishers, Amsterdam, 1992
[25] Mengshoel, O. J., and Goldberg, D. E. Probabilistic crowding: deterministic crowding with probabilistic replacement. In Proceedings of the Genetic and Evolutionary Computation Conference 1999(GECCO-99), Banzhaf, W., et al(eds.) San Fransisco, CA: Morgan Kaufmann. 1999
[26] Goldberg, D. E., and Wang, L. Adaptive niching via co-evolutionary sharing. In Genetic Algorithms in Engineering and Computer Science, Quagliarella, et al.(eds.). John Wiley&Sons, Ltd. 1997:21-38
[27] Schmit L A., Structural optimization- some ideas and insights in: Atrek E, et al (eds). New direction in optimum design, New York: wiley,1984
[28] Vanderplaats G N., Numerical methods for shape optimization: an assessment of the state of the art, in: Atrek E, et al (eds). New direction in optimum design, New York: wiley,1984
[29] Spears, W. M. (2000). Evolutionary algorithms: the role of mutation and recombination. Berlin;New York: Springer.
[30] Kicinger, R., Arciszewski, T., and De Jong, K. A. (2005). "Evolutionary computation and structural design: a survey of the state of the art." Computers & Structures, 83(23-24), 1943-1978.
[31] Schmit, L. A. (1981). Structural synthesis- its genesis and development. AAAI Journal, 19(10), 1249-1263.
[32] Berke, L., & Khot, N. S. (1987). Structural optimization using optimality criteria. In C. A. Mota Soares (Ed.), Computer Aided Optimal Design: Structural and Mechanical System. Berlin: Springer-Verlag, 235-269.
[33] Anagnostou, G., Ronquist, E., & Patera, A. (1992). A computational procedure for part design. Computer Methods in Applied Mechanics and Engineering, 97,33-48.
[34] Kane, C, & Schoenauer, M. (1996). Topological optimum design using genetic algorithms. Control and Cybernetics, 25(5), 1059-1088.
[35] Anagnostou, G., Ronquist, E., & Patera, A. (1992). A computational procedure for part design. Computer Methods in Applied Mechanics and Engineering, 97, 33-48.
[36] Bendsoe, M. P., & Kikuchi, N. (1988). Generating optimal topologies in structural design using a homogenization method. Computer Methods in Applied Mechanics and Engineering,71, 197-224.
[37] Xie, Y. M., & Steven, G. P. (1992). Shape and layout optimization via an evolutionary procedure. In Proceedings of the International Conference on Computational Engineering Science, Hong Kong University of Science and Technology, Hong Kong.
[38] Sandgren, E., Jensen, E. D., & Welton, J. (1990). Topological design of structural components using genetic optimization methods. In Sensitivity Analysis and Optimization with Numerical Methods, AMD-vol. 115, Proceedings of the Winter Annual Meeting of the American Society of Mechanical Engineers. Dallas, TX, 31-43.
[39] Chapman, C. D., Saitou, K., & Jakiela, M. J. (1994). Genetic algorithm as an approach to configuration and topology design. Journal of Mechanical Design, 116,1005-1012.
[40] Jensen, E. D. (1992). Topological structural design using genetic algorithms. Ph.D.Dissertation Purdue University, Lafayette, IN.
[41] Periaux, J., & Winter, G. (Eds.). (1995). Genetic algorithms in engineering and computer science. Chichester, UK: John Wiley.
[42] Schoenauer, M. (1996). Shape representations and evolution schemes. In L. J. Fogel, P. J. Angeline & T. Back (Eds.), Proceedings of the Fifth Annual Conference on Evolutionary Programming, San Diego, CA, USA.
[43] Hamda, H., Jouve, F., Lutton, E., Schoenauer, M., & Sebag, M. (2002). Compact unstructured representations for evolutionary topological optimum design. Applied Intelligence, 16, 139-155
[44] Hamda, H., Roudenko, O., & Schoenauer, M. (2002). Multi-objective evolutionary topological optimum design. In I. C. Parmee (Ed.), Proceedings of the Fifth International Conference on Adaptive Computing Design and Manufacture (ACDM 2002), University of Exeter, Devon, UK, 121-132.
[45] Dorn, W. C, Gomory, R. E., & Greenberg, H. J. (1964). Automatic design of optimal structures. Journal de Mecanique, 3,25-52
[46] Shankar, N., & Hajela, P. (1991). Heuristics driven strategies for near-optimal structural topology development. In B. H. V. Topping (Ed.), Artificial Intelligence and Structural Engineering. Oxford, UK: Civil-Comp Press, 219-226.
[47] Hajela, P., Lee, E., & Lin, C.-Y. (1993). Genetic algorithms in structural topology optimization. In M. P. Bendsoe & C. A. Mota Soares (Eds.), Topology Design of Structures,117-133.
[48] Koumousis, V. K., & Georgiou, P. G. (1994). Genetic algorithms in discrete optimization of steel truss roofs. Journal of Computing in Civil Engineering, 8(3), 309-325.
[49] Bohnenberger, O., Hesser, J., & Manner, R. (1995). Automatic design of truss structures using evolutionary algorithms. In Proceedings of the Second IEEE International Conference on Evolutionary Computation (ICEC'95), Perth, Australia, 143-149.
[50] Rajan, S. D. (1995). Sizing, shape, and topology design optimization of trusses using genetic algorithm. Journal of Structural Engineering, 121,1480-1487.
[51] Rajeev, S., & Krishnamoorthy, C. S. (1997). Genetic algorithms-based methodologies for design optimization of trusses. Journal of Structural Engineering, 123(3), 350-358.
[52] Azid, I. A., Kwan, A. S. K., & Seetharamm, K. N. (2002). An evolutionary approach for layout optimization of a three-dimensional truss. Structural and Multidisciplinary Optimization, 24(4), 333-337.
[53] Bartholomew P, Morris A J, Stars-software package for structure optimization., in: Atrek E, et al (eds). New direction in optimum design, John Wiley & Sons Ltd, 1984:617～632
[54] 隋允康,离散结构断面优化方法和程序系统DDDU-4.上海力学,1987,8(4):12～23
[55] Pham D.T. and Yang Y., Optimization of multi-modal discrete functions using genetic algorithms. In Institute of Mechanical Engineering (part D), 1993, 53～59
[56] Lin C. Y., and Hajela P., EVOLVE: a genetic search based optimization code via multiple strategies, In Hern andez S and Brebbia C. A., editors, Computer Aided optimum Design of Sructures Ⅲ, Optimization of Structural System and Applications, Elsevier Applied Science, 1993,639～654
[57] Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191, 1245-1287.
[58] Michalewicz, Z. (1995). A survey of constraint handling techniques in evolutionary computation methods. In Proceedings of the 4th Annual Conference on Evolutionary Programming, Cambridge, MA, 135-155.
[59] Courant, R. (1943). Variational methods for the solution of problems of equilibrium and vibrations. Bulletin of American Mathematical Society, 49, 1-23. [100] Caroll, C. W. (1961). The created response surface technique for optimizing nonlinear restrained systems. Operations Research, 9, 169-184.
[60] Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning. Reading, Mass.: Addison-Wesley Pub. Co.
[61] Richardson, J. T., Palmer, M. R., Liepins, G. E., & Hilliard, M. R. (1989). Some guidelines for genetic algorithms with penalty functions. In J. D. Schaffer (Ed.), Proceedings of the Third International Conference on Genetic Algorithms (ICGA'89), Faiffax, VA, USA, 191-197.
[62] Goldberg, D. E., & Samtani, M. (1986). Engineering optimization via genetic algorithm. In Proceedings of the Ninth Conference on Electronic Computation, University of Alabama, Birmingham, 471-482.
[63] Joines, J. A., & Houck, C. R. (1994). On the use of non-stationary penalty functions to solve nonlinear constrained optimization problems with GA's. In Z. Michalewicz, J. D. Schaffer, H.-P. Schwefel, D. B. Fogel & H. Kitano (Eds.), Proceedings of the First IEEE International Conference on Evolutionary Computation (ICEC'94), Orlando, FL, USA, 579-584.
[64] Michalewicz, Z., & Attia, N. F. (1994). Evolutionary optimization of constrained problems. In A. V. Sebald & L. J. Fogel (Eds.), Proceedings of the Third Annual Conference on Evolutionary Programming, San Diego, CA, USA, 98-108..
[65] Bean, J. C, & Hadj-Alouane, A. B. (1992). A dual genetic algorithm for bounded integer programs (Technical Report No. TR 92-53): Department of Industrial and Operations Engineering, University of Michingan.
[66] Coello Coello, C. A. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113-127.
[67] Schwefel, H.-P. (1981). Numerical optimization of computer models. Chichester, UK: John Wiley & Sons.
[68] Bean, J. C. (1994). Genetics and random keys for sequencing and optimization. ORSA Journal on Computing, 6,154-160.
[69] Goldberg D. E. and Samtani M.P. Engineering optimization via genetic algorithm, In 9th Conference on Electronic computation, New York, N. Y., ASCE, 1986
[70] Koziel, S., & Michalewicz, Z. (1999). Evolutionary algorithms, homomorphous mappings, and constrained parameter optimization. Evolutionary Computation, 7(1), 19-44.
[71] Michalewicz, Z. (2000). Repair algorithms. In T. Back, D. B. Fogel & Z. Michalewicz (Eds.), Evolutionary computation 2: advanced algorithms and operators (Vol. 2). Bristol and Philadelphia: Institute of Physics Publishing, 56-61.
[72] Kicinger, R., Arciszewski, T., & De Jong, K. A. (2003). Evolutionary designing of steel structures in tall buildings. Journal of Computing in Civil Engineering(tentatively approved).
[73] Paredis, J. (1994). Co-evolutionary constraints satisfaction. In Y. Davidor, H.-P. Schwefel & R. Manner (Eds.), Proceedings of the Third International Conference on Parallel Problem Solving from Nature (PPSN-Ⅲ), Jerusalem, Israel, 46-55
[74] Surry, P. D., Radcliffe, N. J., & Boyd, I. D. (1995). A multi-objective approach to constrained optimization of gas supply networks: the COMOGA method. In T. C. Fogarty (Ed.), Proceedings of the AISB-95 Workshop on Evolutionary Computing, Sheffield, UK,166-180.
[75] Le, T. V. (1995). A fuzzy evolutionary approach to constrained optimization problems. In Proceedings of the Second IEEE International Conference on Evolutionary Computation (ICEC'95), Perth, Australia, 274-278.
[76] Forrest, S., & Perelson, A. S. (1990). Genetic algorithms and the immune system. In H.-P. Schwefel & R. Manner (Eds.), Proceedings of the First International Conference on Parallel Problem Solving from Nature (PPSN-I), Dortmund, Germany, 320-325
[77] Chankong, V., & Haimes, Y. Y. (1983). Multiobjective decision making: theory and methodology. New York: North Holland.
[78] Schaffer, J. D. (1985). Multiple objective optimization with vector evaluated genetic algorithms
[79] Cvetkovic, D., Parmee, I. C, & Webb, E. (1998). Multi-objective optimisation and preliminary airframe design. In I. C. Parmee (Ed.), The Integration of Evolutionary and Adaptive Computing Technologies with Product/System Design and Realisation. Plymouth, UK: Springer-Verlag, 255-267.
[80] Coello Coello, C. A., & Christiansen, A. D. (2000). Multiobjective optimization of trusses using genetic algorithms. Computers & Structures, 75(6), 647-660
[81] Fonseca, C. M., & Fleming, P. J. (1993). Genetic algorithms for multi-objective optimization: formulation, discussion, and generalization. In S. Forrest (Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms (ICGA'93), Urbana-Champaign, IL, USA, 416-423.
[82] Chipperfield, A. J., & Fleming, P. J. (1995). Gas turbine engine controller design using multiobjective genetic algorithms. In A. M. S. Zalzala (Ed.), Proceedings of the First IEE/IEEE International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications (GALESIA'95), Halifax Hall, University of Sheffield, UK.
[83] Obayashi, S. (2002). Pareto solutions of multipoint design of supersonic wings using volutionary algorithms. In I. C. Parmee (Ed.), Adaptive Computing in Design and Manufacture V. London: Springer-Verlag, 3-16.
[84] Grierson, D. E., & Khajehpour, S. (2002). Method for conceptual design applied to office buildings. Journal of Computing in Civil Engineering, 16(2), 83-103.
[85] Srinivas, N., & Deb, K. (1994). Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation, 2(3), 221-248.
[86] Deb, K., Agrawal, S., Pratap, A., & Meyarivan, T. (2000). Fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II. In M. Schoenauer, K. Deb, G. Rudolph, X. Yao, E. Lutton, J. J. Merelo Guervos & H.-P. Schwefel (Eds.), Proceedings of the Sixth International Conference on Parallel Problem Solving from Nature (PPSN-Ⅵ), Paris, France, 849-858.
[87] Hamda, H., Roudenko, O., & Schoenauer, M. (2002). Multi-objective evolutionary topological optimum design. In I. C. Parmee (Ed.), Proceedings of the Fifth International Conference on Adaptive Computing Design and Manufacture (ACDM 2002), University of Exeter, Devon, UK, 121-132
[88] Horn, J., & Nafpliotis, N. (1993). Multiobjective optimization using the niched Pareto genetic algorithm (Technical Report No. IlliGAI Report 93005). Urbana, Illinois, USA: University of Illinois at Urbana-Champaign.
[89] Van Veldhuizen, D. A., & Lamont, G. B. (1998). Multi-onjective evolutionary algorithm research: a history and analysis (No. TR-98-03). Wright-Patterson AFB, Ohio: Department of Electrical and Computer Engineering, Air Force Institute of Technology.
[90] 玄光男,程润伟。遗传算法与工程优化。北京:清华大学出版社,2003
[91] Michalewicz, Z. (1996). Genetic algorithms+data structures=evolution programs (3rd rev. and extended ed.). Berlin;New York: Springer-Verlag.
[92] 徐宗本,高勇。遗传算法过早收敛现象的特征分析及其预防。中国科学E辑,1996,26(4),364—375
[93] 张文修,梁怡。遗传算法的数学基础。西安:西安交通大学出版社,2000
[94] Whitley, D. The Genitor Algorithm and Selection Pressure: Why Rank-Based Allocation Reproduction Trials is Best, In: Schaffer, J. (Editor), Proceeding of the 3rd International Conference on Genetic Algorithm, Morgan Kaufmann Publishers, 1989
[95] Goldberg D E. Genetic Algorithms in Search Optimization and Machine Learning. Massachusetts: Addison-Wesley Press, 1989
[96] Herrera F, Lozano M. Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In: Herrera F, Verdegay JL, eds. Genetic Algorithms and Soft Computing. Physica-Verlag (Studies in Fuzziness and Soft Computing, Vol. 8), 1996. 95～125.
[97] Gantovnik, Vladimir B. and Anderson-Cook, Christine M. and Gurdal, Zafer and Watson, Layne T, A genetic algorithm with memory for mixed discrete-continuous design optimization。Technical Report TR-03-12, Computer Science, Virginia Tech, 2003
[98] Whitley, D. The GENITOR. Algorithm and Selection Pressure: Why Rank-Based Allocation Reproduction Trials is Best, In: Schaffer, J. (Editor), Proceeding of the 3rd International Conference on Genetic Algorithm, Morgan Kaufmann Publishers,1989
[99] Goldberg D E. Genetic Algorithms in Search Optimization and Machine Learning. Massachusetts: Addison-Wesley Press, 1989
[100] 苏小红,杨博,王亚东。基于进化稳定策略的遗传算法。软件学报,2003,14(1):1863-1868
[101] Herrera F, Lozano M. Adaptation of genetic algorithm parameters based on fuzzy logic controllers. In: Herrera F, Verdegay JL, eds. Genetic Algorithms and Soft Computing. Physica-Verlag (Studies in Fuzziness and Soft Computing, Vol. 8), 1996. 95～125.
[102] Caroline Ravise and Michele Sebag_ An advanced evolution should not repeat its past errors. In Thirteenth International Conference on Machine Learning, 1996
[103] 边肇祺,张学工编著:《模式识别》,北京:清华大学出版社,2000
[104] A. Collignon, Multi-modality medical image registration by maximization of mutual information, Ph.D. thesis, Catholic University of Leuven, Leuven, Belgium, 1998.
[105] B. Fei, A. Wheaton, Z. Lee, J. L. Duerk, and D. L. Wilson, "Automatic MR volume registration and its evaluation for the pelvis and prostate," Physics in Medicine and Biology, vol. 47, no. 5, pp. 823-838, 2002.
[106] P. J. Slomka, J. Mandel, D. Downey, and A. Fenster, "Evaluation of voxel-based registration of 3-D power Doppler ultrasound and 3-D magnetic resonance angiographic images of carotid arteries," Ultrasound in Medicine and Biology, vol. 27,no. 7, pp. 945-955, 2001.
[107] P. E. Radau, P. J. Slomka, P. Julin, L. Svensson, and L.-O. Wahlund, "Evaluation of linear registration algorithms for brain SPECT and the errors due to hypoperfusion lesions," Medical Physics, vol. 28, no. 8, pp. 1660-1668, 2001.
[108] W.M. Wells Ⅲ, P.Viola, et al. Multimodal volume registration by maximization of mutual information.Medical Image Analysis, 1996,1(1): 35～51.
[109] 杨虎,马斌荣等,基于互信息的人脑图像配准研究,中国医学物理学杂志,2001,18,69～73
[110] C. Studholme, D. L. G. Hill, and D. J. Hawkes, "An overlap invariant entropy measure of 3D medical image alignment," Pattern Recognition, vol. 32, no. 1, pp. 71-86, 1999
[111] Maes F, Collignon A, Vandermeulen D, et al. Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging, 1997,16(2): 187～198
[112] Spears, W. M. "Adapting Crossover in Evolutionary Algorithms". In Proceeding of the Fourth Annual Conference on Evolutionary Programming in San Diego, CA, 1991
[113] 李芳,凌道盛,工程结构优化设计发展综述。工程设计学报,2002,12 vol,9 NO.5,229～235
[114] Xie Y M, Steven G P. A simple evolutionary procedure for structural optimization[J]. Comput &Struct, 1993,49(5):885—896.
[115] 章毓晋,图像处理与分析。北京:清华大学出版社。1999年3月第一版。
[116] Couro Kane, Marc Schoenauer. Topological Optimum Design using Genetic Algorithms. Control and Cybernetics, 25(5):1059-1088, 1996.
[117] Van Veldhuizen,D.A.(1999). Multiobjective evolutionary algorithms: Classfication,Analysis, and new innovations. PH.D. thesis. AFIT/DS/ENG/99-01, Air force institute of technology, Wright Patterson AFB, Ohio.
[118] Kalyanmoy Deb and Gushar Goel. Multi-Objective Evolutionary Algorithms for Engineering Shape Design.
[119] Rosenberg, R.S. Simulation of genetic population with biochemical properties. Ph.D. Dissertation University of Michigan, Ann Harbor, Michigan, 1967
[120] Schaffer, J.D. Some experiments in machine learning using vector evaluated genetic algorithms. Ph.D. Dissertation Vanderbilt University,Nashville, TN, 1984
[121] Zitzler E,Thiele L. MultiObjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Transactions on evolutionary computation, 1999,3(4):257-271
[122] Zitzler E, Laumanns M, and Thiele L. SPEA2:Improving the Strength Pareto Evolutionary Algorithms.
[123] Silverman,B. Density estimation for statistics and data analysis. London: Chapman and Hall. 1986
[124] 陆鑫达等译。并行程序设计。北京:机械工业出版社,2002.
[125] 陈国良,安虹等。并行算法实践。北京:高等教育出版社,2004.
[126] 郭彤城,慕春棣。并行遗传算法的新进展。系统工程理论与实践,2002年第2期。
[127] ANSI Technical Computing X3H5. 1993, Parallel processing model for high-level programming language.
[128] Gustafson J L. 1988. Reevaluating Amdahl's Law. Comm. Of ACM, 31:532-533
[129] Sun X H and Ni L M. 1990. Another view of parallel speed. Proc. Supercomputing'90
[130] Cantu-Paz, E.(2000).Efficient and Accurate Parallel Genetic Algorithms, Kluwer Academic Publishers, Norwell,MA.
[131] Erick Cantu-Paz. A survey of Parallel Genetic Algorithms.
[132] GROSSO P. B.,Computer simulations of genetic adaptation : Parallel subcomponent subcomponent interaction in a multilocus model doctoral dissertation, The University of Michigan, 1985.
[133] PETTEY C. B., LEUZE M. R., GREFENSTETTE J. J., A parallel genetic algorithm . In GREFENSTETTE J. J., Ed., Proceedings of the Second International Conference。 on Genetic Algorithms, p. 155-161, Lawrence Erlbaum Associates (Hillsdale, NJ), 1987.
[134] MUHLENBEIN H., SCHOMISCHM. , BORN J., 《The Parallel Genetic Algorithm as Function Optimizer 》. In BELEW R. K., BOOKER L. B., Eds., Proceedings of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann (San Mateo, CA), 1991.
[135] PETTEY C. C, LEUZE M. R., A theoretical investigation of a parallel genetic algorithm. In SCHAFFER J. D., Ed., Proceedings of the Third International Conference on Genetic Algorithms, p. 398-405, Morgan Kaufmann (San Mateo, CA), 1989.
[136] BRAUN H. C, On Solving Travelling salesman Problems by Genetic Algorithms. In SCHWEFEL H.-P., MANNER R., Eds., Parallel Problem Solving from Nature, p. 129-133, Springer-Verlag (Berlin), 1990.
[137] MUNETOMO M., TAKAI Y., SATO Y., An efficient migration scheme for subpopulation-based asynchronously parallel genetic algorithms. In FORREST S., Ed., Proceedings of the Fifth International Conference on Genetic Algorithms, page 649, Morgan Kaufmann (San Mateo,CA), 1993.
[138] MUNETOMO M., TAKAI Y., SATO Y.,An efficient migration scheme for subpopulation-based asynchronously parallel genetic algorithms. In FORREST S., Ed., Proceedings of the Fifth International Conference on Genetic Algorithms, page 649, Morgan Kaufmann (San Mateo, CA), 1993.
[139] MARIN F. J., TRELLES-SALAZAR O. , SANDOVAL F., Genetic algorithms on LAN- message passing architectures using PVM : Application to the routing problem. In DAVIDOR Y., SCHWEFEL H.-P., MANNER R., Eds., Parallel Problem Solving fron Nature, PPSN III, p. 534-543, Springer-Verlag (Berlin), 1994.
[140] BESSIERE P., TALBI E.-G..A parallel genetic algorithm for the graph partitioning problem. In ACM Int. Conf. on Supercomputing ICS91, Cologne, Germany, July 1991.
[141] SEREDYNSKI F., Dynamic mapping and load balancing with parallel genetic algorithms. In Proceedings of the First IEEE Conference on Evolutionary Computation, vol. 2, p. 834-839, IEEE Press (Piscataway, NJ), 1994.
[142] DAVIS M., LIU L., ELIAS J. G., VLSI circuit synthesis using a parallel genetic algorithm. In Proceedings of the First IEEE Conference on Evolutionary Computation, vol. 1, p. 104-109, IEEE Press (Piscataway, NJ), 1994.
[143] PunchWF,AverillRC,GoodmanED,etal.Optimal-design of laminated composite structures using coarse-grain parallel genetic algorithms [J]. Comput. Syst.Eng., 1994, 5(4-6):415-423.
[144] 杨堃,并行计算及其在模拟风沙跃移运动中的应用。兰州大学硕士学位论文,固体力学系,2004
[145] 马光文,王黎。遗传算法在桁架结构优化设计中的应用。工程力学,1998;15(2):38～44