基于遗传算法的形状误差计算研究
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摘要
论文对基于遗传算法的形状误差计算进行了系统的深入研究，重点包括实数编码遗传算法理论研究；遗传算法在函数优化方面的应用研究：基于遗传算法的基本几何形体的形状误差计算：基于遗传算法的平面曲线形状误差计算；基于遗传算法的复杂几何形体的形状误差计算。论文的主要内容如下：
1．研究实数编码遗传算法的理论
对实数编码的遗传算法进行了系统的深入研究，提出了基于归一化实数编码遗传算法；讨论了其复制算子、交差算子、变异算子的选取和适应度函数的定义；对基于归一化实数编码遗传算法的模式定理进行了分析和研究；研究了一维和多维归一化实数编码长度与优化精度的关系；用马尔可夫链分析了归一化实数编码的遗传算法的收敛性；探讨了归一化实数编码的遗传算法的计算效率和性能；针对多维寻优问题，提出了基于归一化实数编码多维并行遗传算法，并对其遗传操作算子的机理进行了详细研究。
2．研究归一化实数编码遗传算法的函数优化技术
利用对实数编码遗传算法的研究成果，对函数的优化问题开展了以下研究工作：
1)应用归一化实数编码遗传算法研究一元函数优化问题，对控制参数和遗传算子的选择以及适应度函数的确定进行了探讨，对其收敛速度进行分析，并采用一系列典型的函数对其性能进行测试。
2)应用归一化实数编码多维并行遗传算法研究多元函数优化问题，对控制参数、遗传算子和适应度函数的选择进行了探讨，并对其收敛速度进行了分析，通过一系列典型函数对其性能进行测试，证明采用归一化实数编码多维并行遗传算法求解多维函数优化问题，可加快全局最优解收敛的速度。
3)应用归一化实数编码遗传算法结合惩罚函数法和模拟退火法来实现求解约束优化问题，通过一系列典型函数对其性能进行测试，证明该方法具有较好的收敛性能。
3．基于遗传算法的基本几何形状误差的计算
按照最小区域法的评定准则，基于遗传算法分别建立了描述圆度误差、平面直线度误差、空间直线度误差、平面度误差、圆柱度误差、圆锥度误差、球度误差的数学模型，解决了基于遗传算法的基本几何形体形状误差计算建模问题。采用基于归一化实数编码遗传算法的研究成果，精确计算简单几何形体的形状误差。
4.基于遗传算法的平面曲线形状误差计算的研究
建立了基于遗传算法的平面标准函数曲线形状误差数学模型，借助B样条函数建立了
适合遗传算法计算的复杂平面曲线形状误差数学模型，采用归一化实数编码多维并行遗传算
法进行求解，获得了满足最小区域法的平面曲线形状误差的解。
5.基于遗传算法的复杂几何形体形状误差计算
按照最小区域法评定准则，建立了基于遗传算法的标准参数曲面形状误差数学模型，借
助3次B样条函数建立了复杂曲面形状误差数学模型，采用归一化实数编码多维并行遗传
算法进行求解。大量的算例测试证明，所获得的结果符合最小区域法的评定原则。
This dissertation mainly discussed the research on the theory of Genetic algorithms based on real encoding, the application of Genetic algorithms at function optimization, and the computation of simply form error, flat curve form error, complex surface form error. Follow are mainly study contents in this paper.
1.Study and discussion of Genetic algorithms theory.
This dissertation put up systemic and embedded research for genetic algorithms with real number encoding, Proposed Normalization Real number encoding, then discussed and studied the mechanism of the pattern theory, the definition of fitting function, analyzed and researched copy arithmetic, cross arithmetic and mutation arithmetic with normalization Real number encoding, studied the relation both length of normalization real number encoding and optimization precision, analyzed the convergence of genetic algorithms based on normalization real number encoding with Markov chain, discussed the calculating efficiency and performance of genetic algorithms based on normalization real number encoding. Aimed at mult-dimension optimization problem, proposed the mult-dimension combine genetic algorithms, and particularly studied its copy arithmetic, cross arithmetic and mutation arithmetic.
2. Research of the function optimization technology with genetic algorithms based on normalization real number encoding.
Following study works has been developed in this dissertation at function optimization problem:
l)Studied the optimization problem of single dimension function with genetic algorithms based on normalization real number encoding, discussed the selection of control parameters, genetic arithmetic and fitting function, analyzed its convergence speed, at last test and analyzed the performance by use of a series of typical function.
2) Studied the optimization problem of mult-dimension function with genetic algorithms based on normalization real number encoding, discussed the selection of control parameters, genetic arithmetic and fitting function, analyzed its convergence speed. With the test of a series of typical functions, The author probed that this method can accelerate convergence speed of global optimization solution.
3) Discussed solved the constraint optimization problem by use of combined with genetic algorithms based on normalization real number encoding, penalty function method and simulated annealing algorithm. With The test of a series of typical functions, The author probed that this method has better convergence.
3. Research of the calculating of basic form errors with genetic algorithms.
This dissertation in turn set up the mathematic model of describing circularity error, flat straightness error, space straightness error, flatness error, cylindricity error, taper error and sphere error, it resolved the calculating problems of simplex form error for satisfying minimal zone law.
This dissertation applied the research result of genetic algorithms based on normalization real number encoding to calculating complex form error, it can precision obtain the resolve of simplex form error and be realized easily with computer. This method inaugurated a new route for the data process of simplex form error.
4. Research of calculating flat curve form error with genetic algorithms.
According to minimal zone law, This dissertation set up the mathematic models of describing
flat standard function curve form error, established the mathematic model of describing flat complex curve form error with B-sample function, in turn establish their fitting functions, then calculate form error with mult-dimension parallel genetic algorithms based on normalization real number encoding. The solution satisfy minimal zone law.
5.Calculating of surface form error with genetic algorithms.
According to minimal zone law, This dissertation set up the mathematic models of describing standard parameter surface form error, established the mathematic model of describing complex surface form error with B-sample function, in turn establish their fitting functions, then calculate
1．研究实数编码遗传算法的理论
对实数编码的遗传算法进行了系统的深入研究，提出了基于归一化实数编码遗传算法；讨论了其复制算子、交差算子、变异算子的选取和适应度函数的定义；对基于归一化实数编码遗传算法的模式定理进行了分析和研究；研究了一维和多维归一化实数编码长度与优化精度的关系；用马尔可夫链分析了归一化实数编码的遗传算法的收敛性；探讨了归一化实数编码的遗传算法的计算效率和性能；针对多维寻优问题，提出了基于归一化实数编码多维并行遗传算法，并对其遗传操作算子的机理进行了详细研究。
2．研究归一化实数编码遗传算法的函数优化技术
利用对实数编码遗传算法的研究成果，对函数的优化问题开展了以下研究工作：
1)应用归一化实数编码遗传算法研究一元函数优化问题，对控制参数和遗传算子的选择以及适应度函数的确定进行了探讨，对其收敛速度进行分析，并采用一系列典型的函数对其性能进行测试。
2)应用归一化实数编码多维并行遗传算法研究多元函数优化问题，对控制参数、遗传算子和适应度函数的选择进行了探讨，并对其收敛速度进行了分析，通过一系列典型函数对其性能进行测试，证明采用归一化实数编码多维并行遗传算法求解多维函数优化问题，可加快全局最优解收敛的速度。
3)应用归一化实数编码遗传算法结合惩罚函数法和模拟退火法来实现求解约束优化问题，通过一系列典型函数对其性能进行测试，证明该方法具有较好的收敛性能。
3．基于遗传算法的基本几何形状误差的计算
按照最小区域法的评定准则，基于遗传算法分别建立了描述圆度误差、平面直线度误差、空间直线度误差、平面度误差、圆柱度误差、圆锥度误差、球度误差的数学模型，解决了基于遗传算法的基本几何形体形状误差计算建模问题。采用基于归一化实数编码遗传算法的研究成果，精确计算简单几何形体的形状误差。
4.基于遗传算法的平面曲线形状误差计算的研究
建立了基于遗传算法的平面标准函数曲线形状误差数学模型，借助B样条函数建立了
适合遗传算法计算的复杂平面曲线形状误差数学模型，采用归一化实数编码多维并行遗传算
法进行求解，获得了满足最小区域法的平面曲线形状误差的解。
5.基于遗传算法的复杂几何形体形状误差计算
按照最小区域法评定准则，建立了基于遗传算法的标准参数曲面形状误差数学模型，借
助3次B样条函数建立了复杂曲面形状误差数学模型，采用归一化实数编码多维并行遗传
算法进行求解。大量的算例测试证明，所获得的结果符合最小区域法的评定原则。
This dissertation mainly discussed the research on the theory of Genetic algorithms based on real encoding, the application of Genetic algorithms at function optimization, and the computation of simply form error, flat curve form error, complex surface form error. Follow are mainly study contents in this paper.
1.Study and discussion of Genetic algorithms theory.
This dissertation put up systemic and embedded research for genetic algorithms with real number encoding, Proposed Normalization Real number encoding, then discussed and studied the mechanism of the pattern theory, the definition of fitting function, analyzed and researched copy arithmetic, cross arithmetic and mutation arithmetic with normalization Real number encoding, studied the relation both length of normalization real number encoding and optimization precision, analyzed the convergence of genetic algorithms based on normalization real number encoding with Markov chain, discussed the calculating efficiency and performance of genetic algorithms based on normalization real number encoding. Aimed at mult-dimension optimization problem, proposed the mult-dimension combine genetic algorithms, and particularly studied its copy arithmetic, cross arithmetic and mutation arithmetic.
2. Research of the function optimization technology with genetic algorithms based on normalization real number encoding.
Following study works has been developed in this dissertation at function optimization problem:
l)Studied the optimization problem of single dimension function with genetic algorithms based on normalization real number encoding, discussed the selection of control parameters, genetic arithmetic and fitting function, analyzed its convergence speed, at last test and analyzed the performance by use of a series of typical function.
2) Studied the optimization problem of mult-dimension function with genetic algorithms based on normalization real number encoding, discussed the selection of control parameters, genetic arithmetic and fitting function, analyzed its convergence speed. With the test of a series of typical functions, The author probed that this method can accelerate convergence speed of global optimization solution.
3) Discussed solved the constraint optimization problem by use of combined with genetic algorithms based on normalization real number encoding, penalty function method and simulated annealing algorithm. With The test of a series of typical functions, The author probed that this method has better convergence.
3. Research of the calculating of basic form errors with genetic algorithms.
This dissertation in turn set up the mathematic model of describing circularity error, flat straightness error, space straightness error, flatness error, cylindricity error, taper error and sphere error, it resolved the calculating problems of simplex form error for satisfying minimal zone law.
This dissertation applied the research result of genetic algorithms based on normalization real number encoding to calculating complex form error, it can precision obtain the resolve of simplex form error and be realized easily with computer. This method inaugurated a new route for the data process of simplex form error.
4. Research of calculating flat curve form error with genetic algorithms.
According to minimal zone law, This dissertation set up the mathematic models of describing
flat standard function curve form error, established the mathematic model of describing flat complex curve form error with B-sample function, in turn establish their fitting functions, then calculate form error with mult-dimension parallel genetic algorithms based on normalization real number encoding. The solution satisfy minimal zone law.
5.Calculating of surface form error with genetic algorithms.
According to minimal zone law, This dissertation set up the mathematic models of describing standard parameter surface form error, established the mathematic model of describing complex surface form error with B-sample function, in turn establish their fitting functions, then calculate
引文
1．汪恺，唐保宁．形位公差原理和应用．机械工业出版社，1991，12
2. ISO 1101:1995 Technical drawings—Geometrical tolerancing—Tolerancing of form ,orientation, location and run-out—Generalities, definitions ,symbols, indication on drawings.
3. H.S. Lee and S.W. Kim, Real-time correction of movement errors of a machine axis by multiple null-balancing using Twyman-Green interferometry. Int J Mach Tools Manufact 1995,35(3): 477—486.
4. S.T. Huang, K.C. Fan and J.H. Wu, A new minimum zone method for evaluating flatness errors. Precision Engineering 1993, 15(1):25～32.
5. Fukuda M, Shimokohbe A. Algorithms for form evaluation methods for minimum zone and least squares. Proceedings of the International Symposium on Metrology for Quality Production. Tokyo. 1984. p. 197～202.
6. K. Carr and P. Ferreira, Verification of form tolerances. Part Ⅱ：cylindricity and straightness of a median line. Precision Engng 1995,17(2):144～155
7．施杰等．用最小条件法求解平面度误差．计量技术，1993；(3)
8．张之江，于瀛洁，张善钟．平面度误差最小区域新算法——有序判别法．计量学报，1998(19)：15～21
9．甄恒洲，李玉光，王平江．一种计算空间平面的平面度误差新方法．组合机床与自动化加工技术，2001，10：19～21
10. D.G. Chetwynd, Applications of linear programming to engineering metrology. Proc. Inst. Mech Eng. 199 (1985), p. 93～100.
11. Fukuda M, Shimokohbe A. Algorithms for form evaluation methods for minimum zone and least squares. Proc. Int. Symp. Metrology for Quality Production, Tokyo, 1984, pp. 197～202..
12. M.S. Shunmugam, Comparison of linear and normal deviations of forms of engineering surfaces. Prec. Eng. 9 (1987), p. 96～102.
13. M.T. Traband, S. Joshi, R.A. Wysk and T.M. Cavalier, Evaluation of straightness and flatness tolerances using minimum zone. Manufact. Rev. 2 (1989), pp. 189～195.
14. K. Carr and P. Ferreira, Verification of form tolerances, Part Ⅱ: Cylindricity and straightness of a median line. Prec. Eng. 17 (1995), pp. 144～156.
15. D.S. Suen and C.N. Chang, Application of neural network interval regression method for minimum zone straightness and flatness. Prec. Eng. 20 (1997), pp. 196～207.
16. Huang ST , Fan KC, Wu JH . A new minimum zone method for evaluating straightness errors. Precision Engineering, 1993,15(3):158～165
17．韩祖行．形状误差的优化算法．计量学报，1992，13(4)：245～250
18. Kiwiel, C. Methods of Descent for Non differentiable Optimization. Springer-Verlay Berlin, Heidelberg, 1985
19．王德平．计算最小区域圆及圆度误差的交叉弦线法·哈尔滨理工大学学报，1998，3(4)：14～17
20．刘健等．形位误差包容评定的理论与实践-线性鞍点规划方法的应用．计量学报，1992，13(1)：24～33
21. J.A. Ventura and S. Yeralan, The Minimax Center estimation problem for automated roundness inspection. European Journal of Operation Research, 1989,41 (1): 64～72
22. Lai K, Wang JA. Computational geometry approach to geometric tolerancing. 16th North American Manufacturing Research Conference, University of Illinois, 1988. p. 376～379.
23. Suen D S, Chang C N. Application of neutral network interval regression method for minimum zone circle roundness. Measurement Science & Technology , 1997, 8 (3): 245～252.
24．姚南询，王殿龙，康德纯．回转体零件轮廓度误差评定方法的实用数学模型研究．计量学报，1991，12(4)：262～268
25. Dawson DJW. Cylindricity and its measurement. Int J Mach Tools Manufact 1992 ; 32: 247～53.
26. Lai JY, Chen IH. Minimum zone evaluation of circles and cylinders. Int J Mach Tools Manufact 1996;36(4) :435～1.
27. Hsin-Yi Lai, Wen-Yuh Jywe, Cha'o-Kuang Chen and Chien-Hong Liu. Precision modeling of form errors for cylindricity evaluation using genetic algorithms. Precision Engineering, 2000,24 (4): 310～319
28．熊有伦．球度误差的判别准则和评定方法．计量学报，1990，11(3)：183～189
29．何改云，宋占杰．球度误差评定方法的研究．计量技术，1999(3)：9～10
30．蒋庄德等．球度误差的评定理论、判别方法和测试技术的研究．西安交通大学学报，1991，25(3)：47～54
31．安立邦等．球度误差的包容评定及其最小条件——线性鞍点规划方法的应用．计量技术，1992(3)：1～4
32. T. Kanada, Evaluation of spherical form errors: computation of spherical form error by means of minimum zone method and some examinations with using simulated data. Precision Engineering, 1995, 17(4): 281～289.
33．朱训生，任新生，刘国良等．拟合二次方程球用于测量与评定全球球度的研究．应用科学学报，1997，15(1)：41～47
34．朱训生，任新生，杨建国等．球度最小二乘评定的一般公式．上海交通大学学报，1998，32(5)：43～45
35．刘书桂，叶声华，土冢田忠夫等．用最佳内接或最佳外接圆锥评定锥形零件误差的方法．仪器仪表学报，1997，18(1)：109～112
36. T. Tsukada, T. Kanada and S. Liu,Method for the evaluation of form errors of conical tapered parts, Prec. Engng. , 10, 1, 1988,
37. T. Tsukada, T. Kanada and S. L iu, The development of a computer-aided centring and leveling system for cylindrical form measurement, Prec. Engng., 10,1 , 1988, 13
38．韩祖行．评定圆锥度误差的优化方法．福州大学学报，1994，22(1)：62～65
39．熊有伦 最小区域的统一判别准则和计算机仲裁计量学报，1987,8(2)：128～133
40．刘健等 形位误差包容评定的理论与实践一——线性鞍点规划方法的应用 计量学报，1992，13(1)：24～33
41. Clarke FH. Optimization and Nonsmooth Analysis. New York : Wiley - Inter science, 1983
42．胡新生，周济，马西庚等．形位误差非线性模型的统一判别准则与算法．计量学报，1997，1，18(1)：11～17
43. Murthy. T .S . RandAbdin , S . Z . Minimumzoneevaluation of surfaces Int. J Machtool Des. Res. 1980 ,20 P P123～136
44. Hong, J. T. Analgorithm for flatness calculation fromgeometrical viewpoint. M. Sc. thesis National Taiwan University,1987.
45．夏新涛．最小向量范数评定形状误差．计量技术，1993，(2)：1～3
46．刘健．线性鞍点规划与形位误差的包容评定．计量技术，1990，(9)1～9
47．熊有伦．精密测量的数学方法．中国计量出版社，1989
48．刘文文．以线性逼近算法模式实现形状误差包容评定．中国机械工程，2001，12(增刊)：4～7
49．侯宇．坐标测量机上平面度误差的快速评定．宇航计测技术，1994，13(2)：16～19
50．侯宇．有效集法在形状误差评定中的理论与应用．计量学报，1996，17(1)：70～73
51. S.H. Cheraghi, H.S. Lim and S. Motafalli, Straightness and flatness tolerance evaluation: an optimization approach. Precision Engng, 1996, 18(1):30～37
52. Lai K, Wang J. A computational geometry approach to geometric tolerancing. 16th North American Manufacturing Research Conference, University of Illinois. 1998. p. 376～379
53. G.L. Samuel and M.S. Shunmugam, Evaluation of straightness and flatness error using computational geometric techniques. Computer Aid Des, 1999,31 (13):829～843.
54. S.D. Philips, W.T. Borchardt, W.T. Estler and J. Buttress, The estimation of measurement uncertainty of small circular features measured by coordinate measuring machines. Precision Engng, 1998, 22(2): 87～97.
55. H.T. Yau, Evaluation and uncertainty analysis of vectorial tolerances. Precision Engng, 1997, 20(3 ): 123～137
56. R. Edgeworh and R. Wilhelm, Adaptive sampling for coordinate metrology. Precision Engng, 1999, 23 (3): 144～154.
57. W.S. Kim and S. Raman, On the selection of flatness measurement points in coordinate measuring machine inspection. Int J Mach Tools Manufact, 2000,4 (3) 427～443.
58. Tang S, Hung YY. Fast profilometer for the automatic measurement of t3-D object shapes. Appl Opt 1990, 29(20):3012～3018.
59. Chang M, Ho CS. Phase-measuring profilometry using sinusoidal grating. Exp Mech, 1993, 33(2):117～122.
60. McNeill SR, Sutton MA, Miao Z, Ma J. Measurement of surface profile using digital image correlation. Exp Mech, 1996, 37(1): 13～20.
61. Morimoto Y, Tang L, Seguchi Y. Automated 3D measurement of shape and strain distribution using fourier transform grid method. ICICE Trans 1991 ;E-74:3459～3466.
62. Morimoto Y, Fujigaki. Automated analysis of 3-D shape and surface strain distributions of a moving object using stereo vision. Opt Lasers Engng 1993(18): 195～212.
63. Tong JW, Wang SB, Li HQ. Determination of dynamic three-dimensional displacement fields on a shell surface. Opt Lasers Engng 1991(15):253～265.
64. Sutton MA, Chcng MQ, Peters WH, Chao Y J, McNeill SR. Application of an optimized digital correlation method to planar deformation analysis. Image Vision Comput1986,4(3): 143～151.
65. Luo PF, Chao YJ, Sutton MA, Peters WH. Accurate measurement of three-dimensional displacement in deformable bodies using computer vision. Expl Mech 1993,33(2): 123～132.
66. Luo PF, Chao YJ, Sutton MA. Application of stereo vision to three-dimensional deformation analyses in fracture experiments. Opt Engng 1994,33(3):981～990.
67. Chao Y J, Sutton MA. Accurate measurement of two-and three-dimensional surface deformations for fracture specimens by computer vision. Exp Techniques Fracture1993:59～93.
68．董洪智，林忠钦，陈关龙．曲面轮廓度评定技术的研究．机械设计与研究，2000(2)：16-18
69．石照耀，谢华锟，费业泰．复杂曲面测量技术的研究综述．机械工艺师，2000(11)：38～40
70．侯宇，张竞，崔晨阳．复杂线轮廓度误差评定方法．仪器仪表学报，2001，22(1)：104～106
71．苑国英，冯文澜，邱志慧．在坐标测量机上检测复杂曲面形状误差的理论与方法研究．工程设计，2000(4)：75～77
72．宋震，杨清好．满足最小条件的复杂曲面轮廓度误差评定理论及其评定软件．计量技术，1995(4)：2～4
73. Sahoo. K and Menq. C H. Location of 3D objects having complex sculptured surface using representation and tactile sensing. ASME J., Engng Ind., 1991,113 (1):85～92
74. Adaptive Sampling and mesh generation. Computer-Aided-Design, 1995,27(3):275～279
75．范裕健，赵单贤．用最小区域法评定形状误差的数学模型．西安交通大学学报，1987(4)
76．钱名海．应用鞍点规划研究形位误差评定的理论与方法．大连理工大学博士论文，1992
77．王平江．曲面测量、建模及数控加工集成技术研究．华中理工大学博士论文．1995，11
78. Rentoul AH, Mullinenx G, Medl and AJ. Interpretation of errors from inspection results Computer Integrated Manufacturing Systems, 1994, 7(3): 173～178
79. Akira Kyusojin, Yoshinori Akmoto. A New Method of Best-Fitting on Curved Surface. in:Li Zhu ed. Proceedings of Second International Symposium on Measurement Technologyand Intelligent Instruments. Wuhan, Hubei, P. R. China. Nov., 1993, USA:SPIE, 2101:54～62
80．陈志杨，李江雄，柯映林．基于图形制导的复杂曲面最佳匹配的一种算法．航空学报，2000(21)：279～28 l
81. Holland and J. H, Adaptation in Nature and Artificial Systems, 1st ed., 1975, 2cd. , Cambridge, MA:MTT Press, 1992
82. Rechenberg and I., Evolutionsstrategie: Optimierung Techenischer Systeme nach Prinzipier; der Biologichen Evolution Stuttgart: Frummann Holzboog verlag, 1973
83. Schwefel, H. P., Kybernetische Evolution Als Strategie der Experimentell Forschung in der Strmungstechnik, Diploma Thesis, Technical Univ. of Berlin, 1965
84. Fogel L.J. , Owens A. J. and Walsh M. J., Artificial f Intelligent through Simulated Evolution, New York:John whiley, 1966
85. Bagley J. D., The Behavior of Adaptive System Which Employ Genetic and Correlation Algorithms, Dissertation Abstracts International, 1967, 28(12)
86. Rosonberg P. S.,Simulation of Genetic Populations with Biochemical Properities, [ph D Dissertation] . University of Michigan, No.67-17836,1967
87. Hoilistien R. B., .Artificial Genetic Adaptation in Computer Control System, [ph D Dissertation]. University of Michigan, No. 71-23773,1971
88. De Jong K.A., An Analysis of the Behavior of a Class of Genetic Adaptive Systems, [ph D Dissertation]. University of Michigan, No. 76-9381, 1975
89. Bethek A. D., Comparison of Genetic Algorithms and Gradient-Based Optimizers on Parallel Processors: Efficiency of Use Processing Capacity (technical Report No. 197), Ann Arbor: University of Michigan, Login of computers Group
90. Frantz D.R., Non-linearities in Genetic Adaptive Search, [ph D Dissertation], University of Michigan, Abstract Int. 1972,33(11),5240B-5241B
91. Goldberg D. E., Simple Genetic Algorithms and the Minimal Deceptive Problem, In L. Davis (Ed.), Genetic Algorithms and Simulated Annealing, London Pitman, 1987, 74～78
92. Takahashi Y., Convergence of the simple genetic algorithm to the two-bit problem, IEICE Trans Fundamentals, 1994, E77(5):868～880
93. Yamamura M, Satoh H. and Kobayashi S. , An Markovanalysis of generation alternation models on minimaldeceptive problems, In:Int Conf on Information Sciences: Fuzzy Logic, Intelligent Control & Genetic Algorithm, NC: Duke University, 1997:47～50
94. Goldberg D. E. and Smith R E. , Nonstationary function optimization using genetic algorithms with dominance and diploidy. In:Proc of the 2 nd Int Conf on Genetic Algorithms. NJ:Lawrence Erlbaum Associates, 1987:59～68
95. Srinivas M and Patnaik L M, Adaptive Probabilities of Crossover and Mutation in Genetic Algorithm, IEEE TransSyst Man, Cybern, 1994, 24(4) :656～667
96. Deb K.and Goldberg D. E. , Sufficient conditions for deceptive and easy binary functions, Annals of Mathematics and Artificial Intelligence, 1994, 7(10):385～408
97. Barrios D., Pazos J. and R' ios Jetal, Conditions for convergence if genetic algorithms through Walsh series, Computers and Artificial Intelligence, 1994, 13 (5):441～452
98. Grefenstette J. J. , Deception considered harmful, In: Foundations of Genetic Algorithms. CA:Morgan Kaufmann, 1993:75～91
99. Liepins G E and Vose M. D. , Representational issues in genetic optimization, J of Experimental and Theoretical Artificial Intelligence, 1990,2 (2) :101～115
100．黄焱，蒋培，王嘉松等．基于可调变异算子求解遗传算法的欺骗问题．软件学报，1999， 10(2)：216～219
101. Goldberg D. E.and Segrest P., Finite Markov chain analysis of genetic algorithms, In: Proc of the 2nd Int Confon Genetic Algorithm s. N J: Lawrence Erlbaum Associates, 1987. 1～8
102. Rudolph G.,Convergence analysis of canonical genetic algorithms, IEEE Trans on Neural Networks, 1994, 5(1):96～101
103. Eiben A E, Aarts E H L and Hee K M V. ,Global Convergence of Genetic Algorithms: A Markov Chain Analysis. Schwefel H P, Manner R, Eds, Springer-Verlag, 1990.4～12
104. Fogel D. B. , A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems, Simulation, 1995, 64(6) :397～406
105. Qi X. and Palmieri F., Adaptive Mutation in the Genetic Algorithms, Proc. Of the Sec. Ann. Conf. On Evolutionary Programming, Fogel , D. B , Atmar , W. ,Eds. La Jollg , CA: Evolutionary Programming Society, 1993:192～196
106．罗志军．遗传算法全局收敛性的齐次有限马尔柯夫链分析．系统工程与电子技术，2000，22(1)：73～76
107．何琳等．最优保留遗传算法及其收敛性分析．控制与决策，2000，15(1)：63～66
108．林丹，李敏强，寇纪凇．基于实数编码的遗传算法的收敛性研究．计算机研究与发展，2000，37(11)：1321～1327
109．王丽薇，洪勇，洪家荣．遗传算法的收敛性研究．计算机学报，1996，19(10)：794～797
110. Back T. , The Interaction of Mutation Rate. Selection and Self-Adaption within a Genetic Algorithms in Parallel problem solving from Nature, 2. Amsterdam, North Holland, 1992:84～94
111. Muhlenbein H. , How Genetic Algorithms Really Work, I: Mutation and Hill climbing. In Parallel Problem Solving from Nature. 2. Amsterdam, North Holland, 1992:15～25
112. Suzuki J. A., Markov chain analysis on a genetic algorithm, In:ICGA'93, CA:Morgan Kaufmann, 1993:146～153
113. 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
114. Salomon R., Raising theoretical questions about utility of genetic algorithms, Evolutionary Programming 4, Lecture Notes in Computer Science 1213,1997,275～284
115．史奎凡，陈月辉．提高遗传法收敛速度的方法．信息与控制．，1998，27(4)：289～293
116. Niwa T. and Tanaka M. On the mean convergence time forsimple genetic algorithms, In:1995IEEE Int Conf on Evolutionary Computation.NJ:IEEE Service Center, 1995(1) :373～377
117. Aytug H, Bhattacharrya S. and Koehler G. J., A Markov chain analysis of genetic algorithm swith power of 2 cardinality alphabets, European J of Operational Research, 1996(96): 195～201
118. Goldberg D. E., Genetic Algorithms and Wolsh Function:Part. Deception and Its Analysis, Complex Syst, 1989(3):153～171
119. Vose M. D., Generalizing the notion of schema in genetic algorithms, Artificial Intelligence, 1991, , 50:385～396
120．雷德明．多维实数编码遗传算法．控制与决策，2000，15(2)：239～241
121．唐飞等．十进制编码遗传算法的模式定理研究．小型计算机系统，2000，21(4)：364～367
122. Antonisse J. , A new interpretation of schema notation that overturns the binary encoding constraint, In: Proc of the3 rd Int Conf on Genetic Algorithm s. CA: Morgan Kaufmann, 1989:86～91
123. Antonisse H. J. and Keller K. S. ,Genetic operators for high level knowledge representations,In: Proc of the 2nd Int Conf on Genetic Algorithms. NJ:L awrence Erlbaum Associates, 1987: 69～76
124. Michalewicz Z. etal. , Genetic algorithms and Optimal Control Problems, Proc. 29th, IEEE Conf Dicision and Control, 1990:1664～1666
125. Michalewicz Z. etal. ,A modified Genetic" Algorithm for Optimal Control Problems, Computers Math, Applic., 1992, 2302):83～94
126．张晓绩，方浩，戴冠中．遗传算法编码机制研究．信息于控制，1997，26(1)：134～139
127. Goldberg D. E. and et . al. , Messy Genetic algorithms: Motivation , Analysis and First Results, Complex Syst. 1989(3):493～530
128. Feng Q X and Francesco P., Theoretical Analysis of Evolutionary Algorithms with an lnfinite Population Size in Continuous Space Part and :Basic Properties of Selection and Mutation, IEEE Trans Neural Networks, 1994, 5(1):102～129
129. Janikow C. I. and Michalewicz I. An Experimental Comparison of Binary and Floating Point Representation in Genetic Algorithms, In Proc4th Int Conf Genetic Algorithms, 1991 (3):1～36
130. Robert J. S. and Rovert J. M., Dynamicfuzzycontrol of Genetic Algorithm Parameter Coding, IEEE Tran syst Man, Cybern, 1999,29(3):426～433
131. Goldberg D. E. , Optimal Initial Population Size for Binary-coded Genetic Algorithms, TCGA Report, University of Alabama, 1985
132．孙艳丰，王众托·自然数编码遗传算法的最优群体规模·信息与控制，1996，25(5)：
317～320
133. Davis L. , Adaptive Operator Probabilities in Genetic Algorithms, In Proc3 rd Int Conf Genetic Algorithms, 1989:61～69
134. Fogarty J. C., Varying the Probability of Mutation in Genetic Algorithms, In Proc 3 rd Int Conf Genetic Algorithms, 1989:42～50
135. Whitley D, Starkweather D. ,Genetic :A Distributed Genetic Algorithm, Expt JTheory, Art Intell. 1990,2 : 189～214
136．孙瑞祥，屈梁生．遗传算法进化截止代数分布规律的研究．计算机研究与发展，2000，37(2)：188～193 ．
137. Potts JC, etal.,The Development and Evaluation of an Improved Genetic Algorithm Based on Migration and Artificial Selection, IEEE Trans Syst Man, Cybern, 1994, 24(1):73～86
138. Kuo T. and Hwang S. Y., A Genetic Algorithm with Disruptive Selection, IEEE Trans Syst Man, Cybern, 1996,26(2):299～306
139. Back T and Hoffmeister F. , Extended Selection Mechanisms in Genetic Algorithms, In Proc4th Int ConfGenetic Algorithms, 1991: 89～99
140．田力汉，陈震，田夫汉．遗传算法中交叉算子对群体多样性的影响．计算机工程与科学，2000，22(4)：46～49
141. Feng Q X and Francesco P., Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space Part and :Basic Properties of Selection and Mutation, IEEE Trans Neural Networks, 1994, 5(1) :102～129
142．黄晓峰，潘立登，陈标华．实数编码遗传算法中交叉操作的效率分析．控制与决策，1998， 13(增刊)：496～503
143．章珂，刘贵忠．交叉位置非等概率选取的遗传算法．信息与控制，1997，26(1)：53～60
144．袁慧梅．具有自适应交换率和变异率的遗传算法．首都师范大学学报，2000，21(3)：14～20
145．张良杰，毛志宏，李衍达．遗传算法中突变算子的数学分析及改进策略．电子科学学刊，1996，18(6)：590～595．
146．李强，王彦平．遗传算法中变异对优化进程的影响分析．高技术通讯，2000(8)：73～75
147. Krishnakumar, K.. Micro-Genetic Algorithms for Stationary and Non-Stationary Function Optimization, SPIE Intelligent Control and Adaptive Systems, 1989,1196,289～296
148．辛香非，朱鳌鑫．遗传算法的适应度函数研究．系统工程与电子技术，1998(11)：58～62
149. VASSILIOS P., Varying fitness functions in genetic algorithms constrained optimization: The Cutting Stock and Unit Commitment Problems, IEEE Trans Syst Man, Cybern, 1998, 28(5):629～639
150．胡国四，韩生廉．遗传算法适值函数定义方法的研究．控制与决策，1999，14(6)：694～697
151. Lin E T. , Kao C.Y. and Hsu, C. C. , Applying the Genetic Algorithms Approach to Simulated Annealing in Solving Some NP-Hard Problems . IEEE trans. SMC,1993, 23(6):1753～1767.
152. Ackley D. , A Connectionist Machine for Genetic Hillclimbing, Kluwer Academic Publishers, Boston, 1987
153. Davis, L., Handbook of Genetic Algorithms , Van Nostrand Reinhold, New York ,1991
154. Zeigler B P and Kim J., Asynchronous Genetic Algorithms on Parallel Computers, In Proc 5th Int Conf Genetic Algorithms, 1993
155. Bertoni A and Dorigo M., Implicit Parallelism in Genetic Algorithms, In Art Intell, 1993
156. N. Marco and S. Lanteri, A two-level parallelization strategy for Genetic Algorithms applied to optimum shape design, www.elsevier.com/locate/parco, Parallel Computing ,2000(26): 377～397
157. Erick Cant(?)-Paz, David E. and Goldberg., Efficient parallel genetic algorithms: theory and practice, www.elsevier.com/locate/cma ,Comput. Methods Appl. Mech. Engrg. 2000(186):221～238
158．沈洁陈，李开荣·遗传算法的并行实现·扬州大学学报，2000，3(2)：1～6
159. B. Kroger, P. Schwenderling and O. Vomberger, Parallel Genetic Packing of Rectangles, In:Parallel Problem Solving from Ngture 1. Springer-Verlag, 1991
160. Cavicchio D. J . , Adaptive Search Using Simulated Evolution: Doctoral Dissertation . University of Michigan, Ann ,Arbor, 1970
161. Cavicchio D. J . , Reproductive Adaptive Plans, Proceedings of the ACM 1972 Annual Conference, 1972:1～11
162. Goldberg E. E., Genetic algorithms in search, optimization and machine learning, Reading, MA:Addison Wesley Publishing Company, 1989
163．林焰，郝聚民，纪卓尚等·隔离小生境遗传算法研究·系统工程学报，2000，15(1)：86～91
164. Bosworth J, Foo N and Zeigler B P., Comparison of genetic algorithms with conjugate gradient methods, Michigan: The University of Michigan, 1972
165. Antonisse J. , A new interpretation of schema notation that overturns the binary encoding constraint, In: Proc of the3 rd Int Confon Genetic Algorithm s. CA: Morgan Kaufmann, 1989: 86～91
166. Antonisse H J and Keller K S., Genetic operators for high level knowledge representations, In: Proc of the 2nd Int Conf on Genetic Algorithms. NJ:L awrence Erlbaum Associates,1987:69～76
167. Zeng-Ping Chen, Jian-Hui Jiang and Yang Li, Ru-Qin Yu, Nonlinear mapping using real-valued genetic algorithm, Chemometrics and Intelligent Laboratory Systems 45 1999 409～418
168．孙艳丰，郑加齐，王德兴等·基于遗传算法的约束优化方法评述·北方交通大学学报，2000，24(6)：14～19
169. Homaifar A, Lai SHY and Qi X., Constrained Optimization via Genetic Algorithms. Simulation, 1994,62(4): 242～254.
170．吴志远，邵惠鹤，吴新余·基于遗传算法的退火精确罚函数非线性约束优化方法·控制与决策，1998,13(2)：136～140．
171. Schaffer J D., Multiple objective optimization with vector evaluated genetic algorithms, Proc of International Conference on Genetic Algorithms and Their Applications, 1985.93～100
172. Hajela P and Lin C Y., Genetic search strategies in multicriterion optimal design, Structural Op Iimization, 1992,5(4):99～107
173．林焰，郝聚民，纪卓尚·基于模糊优选的多目标优化遗传算法·系统工程理论与实践，1999(12)：31～37
174. Hajela P and Shih,C J. , Optimal design of laminated composites using a modified mixed integer and discrete programming algorithm, Computer & Structure, 32,213～221
175. Tung Kuan Liu, Toshiyuki Satoh, Tadashi Ishihara and Hikarll lnooka. ,An Application of Genetic Algorithms to Control System Design, Proceedings of the Asian Control Conference
Tokyo, 1994, 27～30
176. Ben Tal, Aharon , Characterization of pareto and lexicographic optimal solutions, In: Multiple Criteria Decision Making Theory and Application (G. Fandeland T. Gal, Eds.) Lecture Notes in Economics and Mathematical Systems Springer Verlag Berlin,1980,177:1～11
177．朱学军，陈彤，薛量等·多个体参与交叉的Pareto多目标遗传算法·电子学报，2001，29(1)：106～109
178. Kasa , J. R.., Hierarchical Genetic Algorithms Operation on Populations of Computer Programs, Proc. Of 11th Int. Joint Conf. On Artificial Intelligence,1989
179. De Jong K.A. , On using Genetic Algorithms to Search Program Space. Proc. 2nd Conf. Genetic Algorithms, 1987:210～216
180. Holland J H And Reiman. J. S. , Cognitive Systems Based on Adaptive Algorithms, In: Waterman D A , Hayes-Roth F(Eds.). Pattern Directed Inference System, New York: Acadmic Press, 1978:313～329
181. Bocker L. B. , GoldBerg D. E. and Holland J. H. , Classifier Systems and Genetic Algorithms,Artificial Intelligence, 1989,40:236～282
182. Smith S. E, Flexible Learning of Problem Solving Heuristics Through Adaptive Search, Proceedings of the 8th International Joint Conference on Artificial Intelligence,1983:422-425
183. Langton C. G.,Study Artificial Life With Cellular Automata, Physisca 22D, 1986,120-149
184. A.H. Mantawy, Youssef L. Abdel-Magid and Shokri Z. Selim.,A new genetic-based tabu search algorithm for unit commitment problem, Electric Power Systems Research 49 (1999) 71～78
185. Lino Costa and Pedro Oliveira, Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems, Computers and Chemical Engineering 25 (2001)257～266
186. Walter A. Kosters, Joost N. Kok and Patrik Flor(?)en, Fourier Analysis of Genetic Algorithms, Theoretical Computer Science 229 (1999) 143～175A. Blanco, M. Delgado and M.C. Pegalajar, A real-coded genetic algorithm for training recurrent neural networks,Neural Networks 14 (2001) 93～105
187. Yi-Chih Hsieh, Ta-Cheng Chen and Dennis L. Bricker, Genetic algorithms for reliabili(y design problems, Microelectronics Reliability 38 (1998) 1599～1605
188. James R. Gremling and Kevin M. Passino, Genetic adaptive state estimation, Engineering Applications of Artificial Intelligence 13 (2000) 611～623
189. Ta-Cheng Chen and Gary W. Fischer, A GA-based search method for the tolerance allocation problem, Artificial Intelligence in Engineering 14 (2000) 133～141
190. Jaydeep Balakrishnan and Chun Hung Cheng , Genetic search and the dynamic layout problem, Computers & Operations Research 27 (2000) 587～593
191. C.A. Coello and A.D. Christiansen , Multiobjective optimization of trusses using genetic algorithms,Computers and Structures 75 (2000) 647～660
192. J.P.B. Leite and B. H. V. Topping, Improved genetic operators for structural engineering optimization, Advances in Engineering Software Vol. 29, No. 7-9, pp. 529-562, 1998 ○ 1998 Elsevier Science Ltd and Civil-Comp Ltd Printed in Great Britain. All rights reserved
0965-9978/98/
193. B.H.V. Topping, J. Sziveri, A. Bahreinejad, J.P.B. Leite and B. Cheng, Parallelprocessing, neural networks and genetic algorithms, Advances in Engineering Software Vol. 29, No. 10, pp. 763-786, 1998○1998 Published by Elsevier Science Ltd Printed in Great Britain. All rights reserved 0965-9978/98/
194. Safaai Deris, Sigeru Omatu, Hiroshi Ohta and Puteh Saad, Incorporating constraint propagation in genetic algorithm for university timetable planning, Engineering Applications of Artificial Intelligence 12 (1999) 241～253
195. D.G. Mayer, J.A. Belward, H. Widell and K. Burrage, Survival of the fittest～genetic algorithms versus evolution strategies in the optimization of systems models, Agricultural Systems 60 (1999) 113～122
196. Peng-Yeng Yin, A new circle/ellipse detector using genetic algorithms, Pattern Recognition Letters 20 (1999) 731～740
197. David William Begg and Xiaojian Liu, On simultaneous optimization of smart structures— Part Ⅱ:Algorithms and examples, Comput Methods Appl. Mech. Engrg. 184 (2000) 25～37
198. Hsin-Yi Lai, Wen-Yuh Jywe, Cha'o-Kuang Chen and Chien-Hong Liu, Precision modeling of form errors for cylindricity evaluation using genetic algorithms, Precision Engineering Journal of the International Societies for Precision Engineering and Nanotechnology 24 (2000)310～319
199. C.L. Karr, Barry Week and L.M. Freeman, Solutions to systems of nonlinear equations via a genetic algorithm, Engineering Applications of Artificial Intelligence 11 (1998) 369～375
200. Yukio Tominaga. Representative subset selection using genetic algorithms. Chemometrics and Intelligent Laboratory Systems 43 1998 157～163
201. Chul Ju Hyun, Yeongho Kim and Yeo Keun Kim, A GENETIC ALGORITHM FOR MULTIPLE OBJECTIVE SEQUENCING PROBLEMS IN MIXED MODEL ASSEMBLY LINES, PⅡ: S0305-0548(98)00026-4, Computers Ops Res. Vol. 25, No. 7/8, pp. 675-690, 1998# 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0305-0548/98
202. P. De Lit, E. Falkenauer and A. Delchambre, Grouping genetic algorithms: an efficient method to solve the cell formation problem, Mathematics and Computers in Simulation 51 (2000) 257～271
203. B.Y. Kim and K.S. Park, Modeling of the nonlinear sequences based on the modified Genetic Algorithm, Nonlinear Analysis 36 (1999) 707～720
204. Yoshikane Takahashi, Convergence of simple genetic algorithms for the two-bit problem. BioSystems 46 (1998) 235～282
205. J. Andre, P. Siarry and T. Dognon, An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization,www.elsevier.corn/locate/advengsoft, Advances in Engineering Software 32 (2001) 49～60
206. Ray J. Paul and Tomas S. Chancy, Simulation optimisation using a genetic algorithm, Simulation Practice and Theory 6 (1998) 601～611
207. Lamia Djerid and Marie-Claude Portmarm, How to keep good schemata using cross-over operators for permutation problems,www.elsevier.com/locate/orms, Intl.Trans.in Op. Res.7 (2000) 637～651
208. Jasmina Arifovic, Evolutionary dynamics of currency substitution,Journal of Economic Dynamics & Control 25 (2001) 395～417
209. Fogel D B, A comparison of evolutionary programming and genetic algorithms on selected constrained optimizationproblems, Simulation, 1995, 64(6):397～406
210. Michalewicz Z etal, Evolutionary algorithms for constrained engineering problems, Computers Ind Engng, 1996, 30 (4) :851～870
211. J.G. NDIRITU, An Improved Genetic Algorithm for Rainfall-Runoff Model Calibration and F'unct ion Optimization, www.elssvier.nl/locate/mcm, Mathematical and Computer Modellii 33 (2001) 696～706
212. E.K. Kemsley, A hybrid classification method: discrete canonical variate analysis using a genetic algorithm, www.elsevier.com/locate/chemometics, Chemometrics and Intelligent Laboratory Systems 55 2001 39～51
213. Chan Ching Yuen, Aatmeeyata, Santosh K. Gupta and Ajay K. Ray, Multi-objective optimization of membrane separation modules using genetic algorithm, Journal of Membrane Science 176 (2000) 177～196
214. Antonio Brunetti, A fast and precise genetic algorithm for a non-linear fitting problem,www.elsevier.nl/Iocate/cpc, Computer Physics Communications 124 (2000) 204～211
215. Michael D. Vose and Jonathan E. Rowe, Random heuristic search: applications to GAs and functions of Unitation, www.elsevier.com/locate/cma, Comput.Methods Appi. Mech. Engrg.186 (2000)195～220
216. D. Greiner, G. Winter and J.M. Emperador, Optimising frame structures by different strategies of genetic algorithms, Finite Elements in Analysis an Design 37 (2001) 381～402
217. Cheng, R. and M. Gen, Genetic Algorithms for Multi-row Machine Layout Problem, Engineering Design and Automation, 1996
218．缪希仁，张培铭．多目标动态优化高级遗传算法及其在智能电磁电器设计中的应用研究．电工技术学报，1999，14(4)：27～30
219．钟守楠．遗传算法的收敛性与编码．武汉水利电力大学学报，2000，33(1)：108～112
220．陈国梁，王煦法，庄镇泉等．遗传算法及其应用．北京：人民邮电出版社，1996．
221．章珂，刘贵忠．交叉位置非等概率选取的遗传算法．信息与控制，1997，6(1)：53～60．
222．孙艳丰，王众托．自然数编码遗传算法的最优群体规模．信息与控制，1996，25(5)：317～320．
223．恽为民，席裕庚．遗传算法的运行机理分析．控制理论与应用，1996，13(3)：297～304．
224．袁宏，王兴华．具有Elitist选择的遗传算法的收敛速度估计．科学通报，1997，42(2)：144～147．
225．席裕庚，柴天佑，恽为民．遗传算法综述．控制理论与应用，1996，13(6)：697～708．
226．谢金星．进化计算简要综述．控制与决策，1997，12(1)：1～7．
227．丁承民，张传生，刘辉．遗传算法纵横谈．信息与控制，1997，26(1)：40～47．
228．孙艳丰，王众托．遗传算法在优化问题中的应用研究．控制与决策，1996，11(4)：425～431．
229．周金荣，黄道，蒋慰孙．遗传算法的改进及其应用研究．控制与决策，1995，10(3)：260～264．
230．黄炯，邬永革，李军等．基于遗传算法的系统在线辨识．信息与控制，1996，25(3)：171～176．
231．田奕，刘涛，李国杰．求解可满足性问题的一种高效遗传算法．模式识别与人工智能，1996．9(3)：209～212
232．朱鳌鑫·遗传算法的适应度函数研究·系统工程与电子技术，1998，(11)：58～62
233．刘勇，康立山，陈毓屏．非数值并行算法(第二册)遗传算法．科学出版社，1995
234．恽为民，席裕庚．遗传算法综述．控制理论与应用，1996，13(6)：697～708
235．张良杰，毛志宏，李衍达．遗传算法中突变算子的数学分析及改进策略．电子科学学刊，1996，18(6)：590～595
236．李逍波，胡庆生，林争辉·一种适用于优化分配问题的对称遗传算法．计算机辅助设计与图形学报，1998，Vol．10,No．1,Jan．，22～28
237．陈建安，郭大伟等．遗传算法理论研究综述．西安电子科技大学学报，1998，Vol．25，No．3，Jun．363～367
238．刘曙光，费佩燕，侯志敏．生物进化论与人工智能中的遗传算法．自然辩证法研究，1999，15(12)：20～24
239．王大明，毛宗源．并行算法综述．济南大学学报(自然科学版)，1998，19(1)：20～25
240．辛，朱鳌鑫．一种求解复杂函数最优解的遗传算法．计算机工程与设计，1998，19(5)：52～58
241．张帆，唐湘蓉．基于遗传算法的优化搜索技术．矿物岩石，1998，增刊：236～238
242．隋洪涛，陈红全．多目标翼型优化设计基因算法研究．空气动力学学报，2000，18(2)：236～240
243．唐亮，刘军，许晓鸣．面向对象的遗传算法工具包及在函数优化中的应用．计算技术与自动化，1999，18(2)：6～9
244．童彤，丰镇平，李军．遗传算法在透平叶栅多目标优化设计中的应用．中国电机工程学报，1999，19(6)：74～76
245．王国庆，郭振邦等．基于改进遗传算法的离散变量结构可靠性优化设计．天津大学学报，1998，31(5)：569～575
246．王英勋，陈宗基．基于遗传算法(GA)的具有约束的飞行轨迹规划．北京航空航天大学，1999，25(3)：355～358
247．王宏刚，曾建潮，徐玉斌．优良模式自学习遗传算法．自动化学报，1999，25(3)：375～379
248．隋洪涛，陈红全，唐智礼．基因算法在喷管反设计中的应用．南京航空航天大学学报，1999，31(2)：127～132．
249．雷德明．自调整遗传算法．系统工程与电子，1999，21(11)：70～71
250．李凡，徐章艳．遗传算法种群多样性的度量．华中理工大学学报，1999，27(7)
251．林丹，寇纪淞，李敏强．遗传规划研究与应用中的若干问题．管理科学学报，2(4)：62～69
252．许海平，张彤，王子才．浮点数编码遗传算法及其在电站机组组合优化种的应用·小型微型计算机系统，1999，20(8)：578～572
253．王熙法．遗传算法及其应用．小型微型计算机系统，1995，16(2)：59～64
254．韩明华，袁乃昌．基于整体退火遗传算法的不等间距天线阵的综合．系统工程与电子技术，1999，21(2)：70～74
255．蒋培，黄炎，杨敬安．关于遗传算法收敛性的分析．湖南师范大学自然科学学报，1999，22(1)：14～18
256．吴少岩，张青富，陈火旺．基于家族优生学的进化算法．软件学报，1997，(2)：137～144
257．张晓馈，戴冠中，徐乃平．遗传算法种群多样性的分析研究．控制理论与应用，1998，15(1)：17～23
258．李海民，吴成柯．遗传算法性能与所求解问题关系的研究．西安电子科技大学学报，1999，26(6)：752～757
259．马丰宁，寇纪淞，李敏强．遗传算法中的满意度与最优距．系统工程理论与实践，1998
(1)：18～21
260．隋洪涛，陈红全．基于B样条的气动反设计遗传算法研究．南京航空航天大学学报，1999，31(1)：18～23
261．刘洪杰，王秀峰，王治宝．利用遗传算法搜索多个极值点．南开大学学报，2000，33(3)：17～22
262．周明，孙树栋，彭炎午．基于遗传模拟退火算法的机器人路径规划．航空学报，1998，19(1)：118～120
263．王宏刚 曾建潮．基于Metropolis判别准则的遗传算法．控制与决策，1998，13(2)：181～184
264．殷铭，张兴华，戴先中．基于MATLAB的遗传算法实现．计算机应用，2000(1)：9～11
265．霍红卫，许进，保铮．选择与变异算子的作用分析．电子学报，2000，28(2)：31～48
266．佘春峰等·浮点遗传算法的收敛性及其在模型参数提取问题中的应用·电子学报，2000，28(3)：134～136
267．雷德明．多维实数编码遗传算法．控制与决策，2000，15(2)：239～241
268．黄昱坤，韩生廉，胡国四．遗传算法非效率操作的改进方法．控制与决策，2000，15(2)：251～253
269．郑金华，蔡自兴．遗传算法中的模式及其转换初探．计算机工程与应用，2000(4)：42～44
270．周琳霞，杨小琴，黎明．动态变异率的多群体进化计算．南昌航空工业学院学报，2000，14(1)：84～86
271．任庆生，叶中行，曾进．基于实数型遗传算法的电子系统可靠性最优分配．通信学报，2000，21(3)：43～46
272．戴晓晖，利敏强，寇纪淞．遗传算法理论研究综述．控制与决策，2000，15(3)：263～273
273．孙瑞祥，屈梁生．遗传算法优化效率的定量评价．自动化学报，2000，26(7)：552～556
274．王健，王建华．标准遗传算法的研究进展．华东船舶工业学院学报，2000，14(3)：29～34
275．钟求喜，谢涛，陈火旺．遗传算法中解个体的生存策略．计算机工程与科学，2000，22(1)：14～17
276．徐进雯，李元春．一种改进编码策略在演化函数优化中的应用．计算机工程与应用，2000，2：49～53
277．汪俐，张铃．用网络实现交叉操作的遗传算法．计算机工程与科学，2000，22(1)：18～20
278．郑金华，陈静，蔡自兴．主从式控制网络并行GA的设计与实现．计算机工程与科学，2000，22(1)：21～24
279．孟祥武，张玉洁．遗传算法交叉操作的可达性．计算机工程与科学，2000，22(1)：25～27
280．孙力娟，吴新余．采用新杂交运算的遗传算法在求解优化问题中的应用．南京邮电学院学报，1997，17(1)：94～97
281．陈海峰等．利用改进遗传算法实现图像特征点的匹配．南京大学学报，2000，36(2)：171～176
282．王健，李露．基于遗传算法的机械方案设计系统的研究．基础自动化，2000，7(2)：57～59
283．基于遗传算法的预测自整定PID控制器．控制与决策，2000，15(1)：113～118
284．赵正佳，黄洪钟，陈新．优化设计求解的遗传—神经网络新算法研究．西南交通大学学报，2000，35(1)：65～68
285．周济等．面向对象的遗传算法及其在神经网络辅助设计中的应用．计算机工程与应用，2000，2：54～56
286．郝翔，李人厚．适用于复杂函数优化的多群体遗传算法．控制与决策，1998，13(3)：263～266
287．马丰宁．遗传算法与遗传规划运行机理的研究．天津大学博士学位论文，1998
288．张铃，张钹．统计遗传算法．软件学报，1997，8(5)：335～344
289. Grefenstette J J, Conditions for implicit parallelism, In:Foundations of Genetic Algorithms. CA:Morgan Kaufmann, 1991:252～261
290. Muhlenbein H. , Evolution in time and space——The parallel genetic algorithm, In:Foundations of Genetic Algorithms. CA:Morgan Kaufmann, 1991:316～337
291. Radcliffe N J, Equivalence class analysis of genetic algorithms, Complex Systems, 1991,5(2):183～205
292. Radcliffe N J, Non-linear genetic representations, ln: Parallel Problem Solving from Nature, Am sterdam: Elsevier Science, 1992:259～268
293. Vose M D and Liepins G E. Schema disruption. In:Proc of the4th Int Conf on Genetic Algorithms. CA:Morgan Kaufm ann, 1991:237～242
294．林于一，沈阳．遗传算法在非线性约束优化问题上的应用．机械设计与研究，1998(2)：13～15
295．李立，李柏林，陈永．BUCHBERGER算法及其在优化设计中的应用．机械设计与研究．1996，(1)：2～4
296．陈国良，王煦法，庄镇泉等．遗传算法及其应用．北京：人民邮电出版社，1996
2. ISO 1101:1995 Technical drawings—Geometrical tolerancing—Tolerancing of form ,orientation, location and run-out—Generalities, definitions ,symbols, indication on drawings.
3. H.S. Lee and S.W. Kim, Real-time correction of movement errors of a machine axis by multiple null-balancing using Twyman-Green interferometry. Int J Mach Tools Manufact 1995,35(3): 477—486.
4. S.T. Huang, K.C. Fan and J.H. Wu, A new minimum zone method for evaluating flatness errors. Precision Engineering 1993, 15(1):25～32.
5. Fukuda M, Shimokohbe A. Algorithms for form evaluation methods for minimum zone and least squares. Proceedings of the International Symposium on Metrology for Quality Production. Tokyo. 1984. p. 197～202.
6. K. Carr and P. Ferreira, Verification of form tolerances. Part Ⅱ：cylindricity and straightness of a median line. Precision Engng 1995,17(2):144～155
7．施杰等．用最小条件法求解平面度误差．计量技术，1993；(3)
8．张之江，于瀛洁，张善钟．平面度误差最小区域新算法——有序判别法．计量学报，1998(19)：15～21
9．甄恒洲，李玉光，王平江．一种计算空间平面的平面度误差新方法．组合机床与自动化加工技术，2001，10：19～21
10. D.G. Chetwynd, Applications of linear programming to engineering metrology. Proc. Inst. Mech Eng. 199 (1985), p. 93～100.
11. Fukuda M, Shimokohbe A. Algorithms for form evaluation methods for minimum zone and least squares. Proc. Int. Symp. Metrology for Quality Production, Tokyo, 1984, pp. 197～202..
12. M.S. Shunmugam, Comparison of linear and normal deviations of forms of engineering surfaces. Prec. Eng. 9 (1987), p. 96～102.
13. M.T. Traband, S. Joshi, R.A. Wysk and T.M. Cavalier, Evaluation of straightness and flatness tolerances using minimum zone. Manufact. Rev. 2 (1989), pp. 189～195.
14. K. Carr and P. Ferreira, Verification of form tolerances, Part Ⅱ: Cylindricity and straightness of a median line. Prec. Eng. 17 (1995), pp. 144～156.
15. D.S. Suen and C.N. Chang, Application of neural network interval regression method for minimum zone straightness and flatness. Prec. Eng. 20 (1997), pp. 196～207.
16. Huang ST , Fan KC, Wu JH . A new minimum zone method for evaluating straightness errors. Precision Engineering, 1993,15(3):158～165
17．韩祖行．形状误差的优化算法．计量学报，1992，13(4)：245～250
18. Kiwiel, C. Methods of Descent for Non differentiable Optimization. Springer-Verlay Berlin, Heidelberg, 1985
19．王德平．计算最小区域圆及圆度误差的交叉弦线法·哈尔滨理工大学学报，1998，3(4)：14～17
20．刘健等．形位误差包容评定的理论与实践-线性鞍点规划方法的应用．计量学报，1992，13(1)：24～33
21. J.A. Ventura and S. Yeralan, The Minimax Center estimation problem for automated roundness inspection. European Journal of Operation Research, 1989,41 (1): 64～72
22. Lai K, Wang JA. Computational geometry approach to geometric tolerancing. 16th North American Manufacturing Research Conference, University of Illinois, 1988. p. 376～379.
23. Suen D S, Chang C N. Application of neutral network interval regression method for minimum zone circle roundness. Measurement Science & Technology , 1997, 8 (3): 245～252.
24．姚南询，王殿龙，康德纯．回转体零件轮廓度误差评定方法的实用数学模型研究．计量学报，1991，12(4)：262～268
25. Dawson DJW. Cylindricity and its measurement. Int J Mach Tools Manufact 1992 ; 32: 247～53.
26. Lai JY, Chen IH. Minimum zone evaluation of circles and cylinders. Int J Mach Tools Manufact 1996;36(4) :435～1.
27. Hsin-Yi Lai, Wen-Yuh Jywe, Cha'o-Kuang Chen and Chien-Hong Liu. Precision modeling of form errors for cylindricity evaluation using genetic algorithms. Precision Engineering, 2000,24 (4): 310～319
28．熊有伦．球度误差的判别准则和评定方法．计量学报，1990，11(3)：183～189
29．何改云，宋占杰．球度误差评定方法的研究．计量技术，1999(3)：9～10
30．蒋庄德等．球度误差的评定理论、判别方法和测试技术的研究．西安交通大学学报，1991，25(3)：47～54
31．安立邦等．球度误差的包容评定及其最小条件——线性鞍点规划方法的应用．计量技术，1992(3)：1～4
32. T. Kanada, Evaluation of spherical form errors: computation of spherical form error by means of minimum zone method and some examinations with using simulated data. Precision Engineering, 1995, 17(4): 281～289.
33．朱训生，任新生，刘国良等．拟合二次方程球用于测量与评定全球球度的研究．应用科学学报，1997，15(1)：41～47
34．朱训生，任新生，杨建国等．球度最小二乘评定的一般公式．上海交通大学学报，1998，32(5)：43～45
35．刘书桂，叶声华，土冢田忠夫等．用最佳内接或最佳外接圆锥评定锥形零件误差的方法．仪器仪表学报，1997，18(1)：109～112
36. T. Tsukada, T. Kanada and S. Liu,Method for the evaluation of form errors of conical tapered parts, Prec. Engng. , 10, 1, 1988,
37. T. Tsukada, T. Kanada and S. L iu, The development of a computer-aided centring and leveling system for cylindrical form measurement, Prec. Engng., 10,1 , 1988, 13
38．韩祖行．评定圆锥度误差的优化方法．福州大学学报，1994，22(1)：62～65
39．熊有伦 最小区域的统一判别准则和计算机仲裁计量学报，1987,8(2)：128～133
40．刘健等 形位误差包容评定的理论与实践一——线性鞍点规划方法的应用 计量学报，1992，13(1)：24～33
41. Clarke FH. Optimization and Nonsmooth Analysis. New York : Wiley - Inter science, 1983
42．胡新生，周济，马西庚等．形位误差非线性模型的统一判别准则与算法．计量学报，1997，1，18(1)：11～17
43. Murthy. T .S . RandAbdin , S . Z . Minimumzoneevaluation of surfaces Int. J Machtool Des. Res. 1980 ,20 P P123～136
44. Hong, J. T. Analgorithm for flatness calculation fromgeometrical viewpoint. M. Sc. thesis National Taiwan University,1987.
45．夏新涛．最小向量范数评定形状误差．计量技术，1993，(2)：1～3
46．刘健．线性鞍点规划与形位误差的包容评定．计量技术，1990，(9)1～9
47．熊有伦．精密测量的数学方法．中国计量出版社，1989
48．刘文文．以线性逼近算法模式实现形状误差包容评定．中国机械工程，2001，12(增刊)：4～7
49．侯宇．坐标测量机上平面度误差的快速评定．宇航计测技术，1994，13(2)：16～19
50．侯宇．有效集法在形状误差评定中的理论与应用．计量学报，1996，17(1)：70～73
51. S.H. Cheraghi, H.S. Lim and S. Motafalli, Straightness and flatness tolerance evaluation: an optimization approach. Precision Engng, 1996, 18(1):30～37
52. Lai K, Wang J. A computational geometry approach to geometric tolerancing. 16th North American Manufacturing Research Conference, University of Illinois. 1998. p. 376～379
53. G.L. Samuel and M.S. Shunmugam, Evaluation of straightness and flatness error using computational geometric techniques. Computer Aid Des, 1999,31 (13):829～843.
54. S.D. Philips, W.T. Borchardt, W.T. Estler and J. Buttress, The estimation of measurement uncertainty of small circular features measured by coordinate measuring machines. Precision Engng, 1998, 22(2): 87～97.
55. H.T. Yau, Evaluation and uncertainty analysis of vectorial tolerances. Precision Engng, 1997, 20(3 ): 123～137
56. R. Edgeworh and R. Wilhelm, Adaptive sampling for coordinate metrology. Precision Engng, 1999, 23 (3): 144～154.
57. W.S. Kim and S. Raman, On the selection of flatness measurement points in coordinate measuring machine inspection. Int J Mach Tools Manufact, 2000,4 (3) 427～443.
58. Tang S, Hung YY. Fast profilometer for the automatic measurement of t3-D object shapes. Appl Opt 1990, 29(20):3012～3018.
59. Chang M, Ho CS. Phase-measuring profilometry using sinusoidal grating. Exp Mech, 1993, 33(2):117～122.
60. McNeill SR, Sutton MA, Miao Z, Ma J. Measurement of surface profile using digital image correlation. Exp Mech, 1996, 37(1): 13～20.
61. Morimoto Y, Tang L, Seguchi Y. Automated 3D measurement of shape and strain distribution using fourier transform grid method. ICICE Trans 1991 ;E-74:3459～3466.
62. Morimoto Y, Fujigaki. Automated analysis of 3-D shape and surface strain distributions of a moving object using stereo vision. Opt Lasers Engng 1993(18): 195～212.
63. Tong JW, Wang SB, Li HQ. Determination of dynamic three-dimensional displacement fields on a shell surface. Opt Lasers Engng 1991(15):253～265.
64. Sutton MA, Chcng MQ, Peters WH, Chao Y J, McNeill SR. Application of an optimized digital correlation method to planar deformation analysis. Image Vision Comput1986,4(3): 143～151.
65. Luo PF, Chao YJ, Sutton MA, Peters WH. Accurate measurement of three-dimensional displacement in deformable bodies using computer vision. Expl Mech 1993,33(2): 123～132.
66. Luo PF, Chao YJ, Sutton MA. Application of stereo vision to three-dimensional deformation analyses in fracture experiments. Opt Engng 1994,33(3):981～990.
67. Chao Y J, Sutton MA. Accurate measurement of two-and three-dimensional surface deformations for fracture specimens by computer vision. Exp Techniques Fracture1993:59～93.
68．董洪智，林忠钦，陈关龙．曲面轮廓度评定技术的研究．机械设计与研究，2000(2)：16-18
69．石照耀，谢华锟，费业泰．复杂曲面测量技术的研究综述．机械工艺师，2000(11)：38～40
70．侯宇，张竞，崔晨阳．复杂线轮廓度误差评定方法．仪器仪表学报，2001，22(1)：104～106
71．苑国英，冯文澜，邱志慧．在坐标测量机上检测复杂曲面形状误差的理论与方法研究．工程设计，2000(4)：75～77
72．宋震，杨清好．满足最小条件的复杂曲面轮廓度误差评定理论及其评定软件．计量技术，1995(4)：2～4
73. Sahoo. K and Menq. C H. Location of 3D objects having complex sculptured surface using representation and tactile sensing. ASME J., Engng Ind., 1991,113 (1):85～92
74. Adaptive Sampling and mesh generation. Computer-Aided-Design, 1995,27(3):275～279
75．范裕健，赵单贤．用最小区域法评定形状误差的数学模型．西安交通大学学报，1987(4)
76．钱名海．应用鞍点规划研究形位误差评定的理论与方法．大连理工大学博士论文，1992
77．王平江．曲面测量、建模及数控加工集成技术研究．华中理工大学博士论文．1995，11
78. Rentoul AH, Mullinenx G, Medl and AJ. Interpretation of errors from inspection results Computer Integrated Manufacturing Systems, 1994, 7(3): 173～178
79. Akira Kyusojin, Yoshinori Akmoto. A New Method of Best-Fitting on Curved Surface. in:Li Zhu ed. Proceedings of Second International Symposium on Measurement Technologyand Intelligent Instruments. Wuhan, Hubei, P. R. China. Nov., 1993, USA:SPIE, 2101:54～62
80．陈志杨，李江雄，柯映林．基于图形制导的复杂曲面最佳匹配的一种算法．航空学报，2000(21)：279～28 l
81. Holland and J. H, Adaptation in Nature and Artificial Systems, 1st ed., 1975, 2cd. , Cambridge, MA:MTT Press, 1992
82. Rechenberg and I., Evolutionsstrategie: Optimierung Techenischer Systeme nach Prinzipier; der Biologichen Evolution Stuttgart: Frummann Holzboog verlag, 1973
83. Schwefel, H. P., Kybernetische Evolution Als Strategie der Experimentell Forschung in der Strmungstechnik, Diploma Thesis, Technical Univ. of Berlin, 1965
84. Fogel L.J. , Owens A. J. and Walsh M. J., Artificial f Intelligent through Simulated Evolution, New York:John whiley, 1966
85. Bagley J. D., The Behavior of Adaptive System Which Employ Genetic and Correlation Algorithms, Dissertation Abstracts International, 1967, 28(12)
86. Rosonberg P. S.,Simulation of Genetic Populations with Biochemical Properities, [ph D Dissertation] . University of Michigan, No.67-17836,1967
87. Hoilistien R. B., .Artificial Genetic Adaptation in Computer Control System, [ph D Dissertation]. University of Michigan, No. 71-23773,1971
88. De Jong K.A., An Analysis of the Behavior of a Class of Genetic Adaptive Systems, [ph D Dissertation]. University of Michigan, No. 76-9381, 1975
89. Bethek A. D., Comparison of Genetic Algorithms and Gradient-Based Optimizers on Parallel Processors: Efficiency of Use Processing Capacity (technical Report No. 197), Ann Arbor: University of Michigan, Login of computers Group
90. Frantz D.R., Non-linearities in Genetic Adaptive Search, [ph D Dissertation], University of Michigan, Abstract Int. 1972,33(11),5240B-5241B
91. Goldberg D. E., Simple Genetic Algorithms and the Minimal Deceptive Problem, In L. Davis (Ed.), Genetic Algorithms and Simulated Annealing, London Pitman, 1987, 74～78
92. Takahashi Y., Convergence of the simple genetic algorithm to the two-bit problem, IEICE Trans Fundamentals, 1994, E77(5):868～880
93. Yamamura M, Satoh H. and Kobayashi S. , An Markovanalysis of generation alternation models on minimaldeceptive problems, In:Int Conf on Information Sciences: Fuzzy Logic, Intelligent Control & Genetic Algorithm, NC: Duke University, 1997:47～50
94. Goldberg D. E. and Smith R E. , Nonstationary function optimization using genetic algorithms with dominance and diploidy. In:Proc of the 2 nd Int Conf on Genetic Algorithms. NJ:Lawrence Erlbaum Associates, 1987:59～68
95. Srinivas M and Patnaik L M, Adaptive Probabilities of Crossover and Mutation in Genetic Algorithm, IEEE TransSyst Man, Cybern, 1994, 24(4) :656～667
96. Deb K.and Goldberg D. E. , Sufficient conditions for deceptive and easy binary functions, Annals of Mathematics and Artificial Intelligence, 1994, 7(10):385～408
97. Barrios D., Pazos J. and R' ios Jetal, Conditions for convergence if genetic algorithms through Walsh series, Computers and Artificial Intelligence, 1994, 13 (5):441～452
98. Grefenstette J. J. , Deception considered harmful, In: Foundations of Genetic Algorithms. CA:Morgan Kaufmann, 1993:75～91
99. Liepins G E and Vose M. D. , Representational issues in genetic optimization, J of Experimental and Theoretical Artificial Intelligence, 1990,2 (2) :101～115
100．黄焱，蒋培，王嘉松等．基于可调变异算子求解遗传算法的欺骗问题．软件学报，1999， 10(2)：216～219
101. Goldberg D. E.and Segrest P., Finite Markov chain analysis of genetic algorithms, In: Proc of the 2nd Int Confon Genetic Algorithm s. N J: Lawrence Erlbaum Associates, 1987. 1～8
102. Rudolph G.,Convergence analysis of canonical genetic algorithms, IEEE Trans on Neural Networks, 1994, 5(1):96～101
103. Eiben A E, Aarts E H L and Hee K M V. ,Global Convergence of Genetic Algorithms: A Markov Chain Analysis. Schwefel H P, Manner R, Eds, Springer-Verlag, 1990.4～12
104. Fogel D. B. , A comparison of evolutionary programming and genetic algorithms on selected constrained optimization problems, Simulation, 1995, 64(6) :397～406
105. Qi X. and Palmieri F., Adaptive Mutation in the Genetic Algorithms, Proc. Of the Sec. Ann. Conf. On Evolutionary Programming, Fogel , D. B , Atmar , W. ,Eds. La Jollg , CA: Evolutionary Programming Society, 1993:192～196
106．罗志军．遗传算法全局收敛性的齐次有限马尔柯夫链分析．系统工程与电子技术，2000，22(1)：73～76
107．何琳等．最优保留遗传算法及其收敛性分析．控制与决策，2000，15(1)：63～66
108．林丹，李敏强，寇纪凇．基于实数编码的遗传算法的收敛性研究．计算机研究与发展，2000，37(11)：1321～1327
109．王丽薇，洪勇，洪家荣．遗传算法的收敛性研究．计算机学报，1996，19(10)：794～797
110. Back T. , The Interaction of Mutation Rate. Selection and Self-Adaption within a Genetic Algorithms in Parallel problem solving from Nature, 2. Amsterdam, North Holland, 1992:84～94
111. Muhlenbein H. , How Genetic Algorithms Really Work, I: Mutation and Hill climbing. In Parallel Problem Solving from Nature. 2. Amsterdam, North Holland, 1992:15～25
112. Suzuki J. A., Markov chain analysis on a genetic algorithm, In:ICGA'93, CA:Morgan Kaufmann, 1993:146～153
113. 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
114. Salomon R., Raising theoretical questions about utility of genetic algorithms, Evolutionary Programming 4, Lecture Notes in Computer Science 1213,1997,275～284
115．史奎凡，陈月辉．提高遗传法收敛速度的方法．信息与控制．，1998，27(4)：289～293
116. Niwa T. and Tanaka M. On the mean convergence time forsimple genetic algorithms, In:1995IEEE Int Conf on Evolutionary Computation.NJ:IEEE Service Center, 1995(1) :373～377
117. Aytug H, Bhattacharrya S. and Koehler G. J., A Markov chain analysis of genetic algorithm swith power of 2 cardinality alphabets, European J of Operational Research, 1996(96): 195～201
118. Goldberg D. E., Genetic Algorithms and Wolsh Function:Part. Deception and Its Analysis, Complex Syst, 1989(3):153～171
119. Vose M. D., Generalizing the notion of schema in genetic algorithms, Artificial Intelligence, 1991, , 50:385～396
120．雷德明．多维实数编码遗传算法．控制与决策，2000，15(2)：239～241
121．唐飞等．十进制编码遗传算法的模式定理研究．小型计算机系统，2000，21(4)：364～367
122. Antonisse J. , A new interpretation of schema notation that overturns the binary encoding constraint, In: Proc of the3 rd Int Conf on Genetic Algorithm s. CA: Morgan Kaufmann, 1989:86～91
123. Antonisse H. J. and Keller K. S. ,Genetic operators for high level knowledge representations,In: Proc of the 2nd Int Conf on Genetic Algorithms. NJ:L awrence Erlbaum Associates, 1987: 69～76
124. Michalewicz Z. etal. , Genetic algorithms and Optimal Control Problems, Proc. 29th, IEEE Conf Dicision and Control, 1990:1664～1666
125. Michalewicz Z. etal. ,A modified Genetic" Algorithm for Optimal Control Problems, Computers Math, Applic., 1992, 2302):83～94
126．张晓绩，方浩，戴冠中．遗传算法编码机制研究．信息于控制，1997，26(1)：134～139
127. Goldberg D. E. and et . al. , Messy Genetic algorithms: Motivation , Analysis and First Results, Complex Syst. 1989(3):493～530
128. Feng Q X and Francesco P., Theoretical Analysis of Evolutionary Algorithms with an lnfinite Population Size in Continuous Space Part and :Basic Properties of Selection and Mutation, IEEE Trans Neural Networks, 1994, 5(1):102～129
129. Janikow C. I. and Michalewicz I. An Experimental Comparison of Binary and Floating Point Representation in Genetic Algorithms, In Proc4th Int Conf Genetic Algorithms, 1991 (3):1～36
130. Robert J. S. and Rovert J. M., Dynamicfuzzycontrol of Genetic Algorithm Parameter Coding, IEEE Tran syst Man, Cybern, 1999,29(3):426～433
131. Goldberg D. E. , Optimal Initial Population Size for Binary-coded Genetic Algorithms, TCGA Report, University of Alabama, 1985
132．孙艳丰，王众托·自然数编码遗传算法的最优群体规模·信息与控制，1996，25(5)：
317～320
133. Davis L. , Adaptive Operator Probabilities in Genetic Algorithms, In Proc3 rd Int Conf Genetic Algorithms, 1989:61～69
134. Fogarty J. C., Varying the Probability of Mutation in Genetic Algorithms, In Proc 3 rd Int Conf Genetic Algorithms, 1989:42～50
135. Whitley D, Starkweather D. ,Genetic :A Distributed Genetic Algorithm, Expt JTheory, Art Intell. 1990,2 : 189～214
136．孙瑞祥，屈梁生．遗传算法进化截止代数分布规律的研究．计算机研究与发展，2000，37(2)：188～193 ．
137. Potts JC, etal.,The Development and Evaluation of an Improved Genetic Algorithm Based on Migration and Artificial Selection, IEEE Trans Syst Man, Cybern, 1994, 24(1):73～86
138. Kuo T. and Hwang S. Y., A Genetic Algorithm with Disruptive Selection, IEEE Trans Syst Man, Cybern, 1996,26(2):299～306
139. Back T and Hoffmeister F. , Extended Selection Mechanisms in Genetic Algorithms, In Proc4th Int ConfGenetic Algorithms, 1991: 89～99
140．田力汉，陈震，田夫汉．遗传算法中交叉算子对群体多样性的影响．计算机工程与科学，2000，22(4)：46～49
141. Feng Q X and Francesco P., Theoretical Analysis of Evolutionary Algorithms with an Infinite Population Size in Continuous Space Part and :Basic Properties of Selection and Mutation, IEEE Trans Neural Networks, 1994, 5(1) :102～129
142．黄晓峰，潘立登，陈标华．实数编码遗传算法中交叉操作的效率分析．控制与决策，1998， 13(增刊)：496～503
143．章珂，刘贵忠．交叉位置非等概率选取的遗传算法．信息与控制，1997，26(1)：53～60
144．袁慧梅．具有自适应交换率和变异率的遗传算法．首都师范大学学报，2000，21(3)：14～20
145．张良杰，毛志宏，李衍达．遗传算法中突变算子的数学分析及改进策略．电子科学学刊，1996，18(6)：590～595．
146．李强，王彦平．遗传算法中变异对优化进程的影响分析．高技术通讯，2000(8)：73～75
147. Krishnakumar, K.. Micro-Genetic Algorithms for Stationary and Non-Stationary Function Optimization, SPIE Intelligent Control and Adaptive Systems, 1989,1196,289～296
148．辛香非，朱鳌鑫．遗传算法的适应度函数研究．系统工程与电子技术，1998(11)：58～62
149. VASSILIOS P., Varying fitness functions in genetic algorithms constrained optimization: The Cutting Stock and Unit Commitment Problems, IEEE Trans Syst Man, Cybern, 1998, 28(5):629～639
150．胡国四，韩生廉．遗传算法适值函数定义方法的研究．控制与决策，1999，14(6)：694～697
151. Lin E T. , Kao C.Y. and Hsu, C. C. , Applying the Genetic Algorithms Approach to Simulated Annealing in Solving Some NP-Hard Problems . IEEE trans. SMC,1993, 23(6):1753～1767.
152. Ackley D. , A Connectionist Machine for Genetic Hillclimbing, Kluwer Academic Publishers, Boston, 1987
153. Davis, L., Handbook of Genetic Algorithms , Van Nostrand Reinhold, New York ,1991
154. Zeigler B P and Kim J., Asynchronous Genetic Algorithms on Parallel Computers, In Proc 5th Int Conf Genetic Algorithms, 1993
155. Bertoni A and Dorigo M., Implicit Parallelism in Genetic Algorithms, In Art Intell, 1993
156. N. Marco and S. Lanteri, A two-level parallelization strategy for Genetic Algorithms applied to optimum shape design, www.elsevier.com/locate/parco, Parallel Computing ,2000(26): 377～397
157. Erick Cant(?)-Paz, David E. and Goldberg., Efficient parallel genetic algorithms: theory and practice, www.elsevier.com/locate/cma ,Comput. Methods Appl. Mech. Engrg. 2000(186):221～238
158．沈洁陈，李开荣·遗传算法的并行实现·扬州大学学报，2000，3(2)：1～6
159. B. Kroger, P. Schwenderling and O. Vomberger, Parallel Genetic Packing of Rectangles, In:Parallel Problem Solving from Ngture 1. Springer-Verlag, 1991
160. Cavicchio D. J . , Adaptive Search Using Simulated Evolution: Doctoral Dissertation . University of Michigan, Ann ,Arbor, 1970
161. Cavicchio D. J . , Reproductive Adaptive Plans, Proceedings of the ACM 1972 Annual Conference, 1972:1～11
162. Goldberg E. E., Genetic algorithms in search, optimization and machine learning, Reading, MA:Addison Wesley Publishing Company, 1989
163．林焰，郝聚民，纪卓尚等·隔离小生境遗传算法研究·系统工程学报，2000，15(1)：86～91
164. Bosworth J, Foo N and Zeigler B P., Comparison of genetic algorithms with conjugate gradient methods, Michigan: The University of Michigan, 1972
165. Antonisse J. , A new interpretation of schema notation that overturns the binary encoding constraint, In: Proc of the3 rd Int Confon Genetic Algorithm s. CA: Morgan Kaufmann, 1989: 86～91
166. Antonisse H J and Keller K S., Genetic operators for high level knowledge representations, In: Proc of the 2nd Int Conf on Genetic Algorithms. NJ:L awrence Erlbaum Associates,1987:69～76
167. Zeng-Ping Chen, Jian-Hui Jiang and Yang Li, Ru-Qin Yu, Nonlinear mapping using real-valued genetic algorithm, Chemometrics and Intelligent Laboratory Systems 45 1999 409～418
168．孙艳丰，郑加齐，王德兴等·基于遗传算法的约束优化方法评述·北方交通大学学报，2000，24(6)：14～19
169. Homaifar A, Lai SHY and Qi X., Constrained Optimization via Genetic Algorithms. Simulation, 1994,62(4): 242～254.
170．吴志远，邵惠鹤，吴新余·基于遗传算法的退火精确罚函数非线性约束优化方法·控制与决策，1998,13(2)：136～140．
171. Schaffer J D., Multiple objective optimization with vector evaluated genetic algorithms, Proc of International Conference on Genetic Algorithms and Their Applications, 1985.93～100
172. Hajela P and Lin C Y., Genetic search strategies in multicriterion optimal design, Structural Op Iimization, 1992,5(4):99～107
173．林焰，郝聚民，纪卓尚·基于模糊优选的多目标优化遗传算法·系统工程理论与实践，1999(12)：31～37
174. Hajela P and Shih,C J. , Optimal design of laminated composites using a modified mixed integer and discrete programming algorithm, Computer & Structure, 32,213～221
175. Tung Kuan Liu, Toshiyuki Satoh, Tadashi Ishihara and Hikarll lnooka. ,An Application of Genetic Algorithms to Control System Design, Proceedings of the Asian Control Conference
Tokyo, 1994, 27～30
176. Ben Tal, Aharon , Characterization of pareto and lexicographic optimal solutions, In: Multiple Criteria Decision Making Theory and Application (G. Fandeland T. Gal, Eds.) Lecture Notes in Economics and Mathematical Systems Springer Verlag Berlin,1980,177:1～11
177．朱学军，陈彤，薛量等·多个体参与交叉的Pareto多目标遗传算法·电子学报，2001，29(1)：106～109
178. Kasa , J. R.., Hierarchical Genetic Algorithms Operation on Populations of Computer Programs, Proc. Of 11th Int. Joint Conf. On Artificial Intelligence,1989
179. De Jong K.A. , On using Genetic Algorithms to Search Program Space. Proc. 2nd Conf. Genetic Algorithms, 1987:210～216
180. Holland J H And Reiman. J. S. , Cognitive Systems Based on Adaptive Algorithms, In: Waterman D A , Hayes-Roth F(Eds.). Pattern Directed Inference System, New York: Acadmic Press, 1978:313～329
181. Bocker L. B. , GoldBerg D. E. and Holland J. H. , Classifier Systems and Genetic Algorithms,Artificial Intelligence, 1989,40:236～282
182. Smith S. E, Flexible Learning of Problem Solving Heuristics Through Adaptive Search, Proceedings of the 8th International Joint Conference on Artificial Intelligence,1983:422-425
183. Langton C. G.,Study Artificial Life With Cellular Automata, Physisca 22D, 1986,120-149
184. A.H. Mantawy, Youssef L. Abdel-Magid and Shokri Z. Selim.,A new genetic-based tabu search algorithm for unit commitment problem, Electric Power Systems Research 49 (1999) 71～78
185. Lino Costa and Pedro Oliveira, Evolutionary algorithms approach to the solution of mixed integer non-linear programming problems, Computers and Chemical Engineering 25 (2001)257～266
186. Walter A. Kosters, Joost N. Kok and Patrik Flor(?)en, Fourier Analysis of Genetic Algorithms, Theoretical Computer Science 229 (1999) 143～175A. Blanco, M. Delgado and M.C. Pegalajar, A real-coded genetic algorithm for training recurrent neural networks,Neural Networks 14 (2001) 93～105
187. Yi-Chih Hsieh, Ta-Cheng Chen and Dennis L. Bricker, Genetic algorithms for reliabili(y design problems, Microelectronics Reliability 38 (1998) 1599～1605
188. James R. Gremling and Kevin M. Passino, Genetic adaptive state estimation, Engineering Applications of Artificial Intelligence 13 (2000) 611～623
189. Ta-Cheng Chen and Gary W. Fischer, A GA-based search method for the tolerance allocation problem, Artificial Intelligence in Engineering 14 (2000) 133～141
190. Jaydeep Balakrishnan and Chun Hung Cheng , Genetic search and the dynamic layout problem, Computers & Operations Research 27 (2000) 587～593
191. C.A. Coello and A.D. Christiansen , Multiobjective optimization of trusses using genetic algorithms,Computers and Structures 75 (2000) 647～660
192. J.P.B. Leite and B. H. V. Topping, Improved genetic operators for structural engineering optimization, Advances in Engineering Software Vol. 29, No. 7-9, pp. 529-562, 1998 ○ 1998 Elsevier Science Ltd and Civil-Comp Ltd Printed in Great Britain. All rights reserved
0965-9978/98/
193. B.H.V. Topping, J. Sziveri, A. Bahreinejad, J.P.B. Leite and B. Cheng, Parallelprocessing, neural networks and genetic algorithms, Advances in Engineering Software Vol. 29, No. 10, pp. 763-786, 1998○1998 Published by Elsevier Science Ltd Printed in Great Britain. All rights reserved 0965-9978/98/
194. Safaai Deris, Sigeru Omatu, Hiroshi Ohta and Puteh Saad, Incorporating constraint propagation in genetic algorithm for university timetable planning, Engineering Applications of Artificial Intelligence 12 (1999) 241～253
195. D.G. Mayer, J.A. Belward, H. Widell and K. Burrage, Survival of the fittest～genetic algorithms versus evolution strategies in the optimization of systems models, Agricultural Systems 60 (1999) 113～122
196. Peng-Yeng Yin, A new circle/ellipse detector using genetic algorithms, Pattern Recognition Letters 20 (1999) 731～740
197. David William Begg and Xiaojian Liu, On simultaneous optimization of smart structures— Part Ⅱ:Algorithms and examples, Comput Methods Appl. Mech. Engrg. 184 (2000) 25～37
198. Hsin-Yi Lai, Wen-Yuh Jywe, Cha'o-Kuang Chen and Chien-Hong Liu, Precision modeling of form errors for cylindricity evaluation using genetic algorithms, Precision Engineering Journal of the International Societies for Precision Engineering and Nanotechnology 24 (2000)310～319
199. C.L. Karr, Barry Week and L.M. Freeman, Solutions to systems of nonlinear equations via a genetic algorithm, Engineering Applications of Artificial Intelligence 11 (1998) 369～375
200. Yukio Tominaga. Representative subset selection using genetic algorithms. Chemometrics and Intelligent Laboratory Systems 43 1998 157～163
201. Chul Ju Hyun, Yeongho Kim and Yeo Keun Kim, A GENETIC ALGORITHM FOR MULTIPLE OBJECTIVE SEQUENCING PROBLEMS IN MIXED MODEL ASSEMBLY LINES, PⅡ: S0305-0548(98)00026-4, Computers Ops Res. Vol. 25, No. 7/8, pp. 675-690, 1998# 1998 Elsevier Science Ltd. All rights reserved Printed in Great Britain 0305-0548/98
202. P. De Lit, E. Falkenauer and A. Delchambre, Grouping genetic algorithms: an efficient method to solve the cell formation problem, Mathematics and Computers in Simulation 51 (2000) 257～271
203. B.Y. Kim and K.S. Park, Modeling of the nonlinear sequences based on the modified Genetic Algorithm, Nonlinear Analysis 36 (1999) 707～720
204. Yoshikane Takahashi, Convergence of simple genetic algorithms for the two-bit problem. BioSystems 46 (1998) 235～282
205. J. Andre, P. Siarry and T. Dognon, An improvement of the standard genetic algorithm fighting premature convergence in continuous optimization,www.elsevier.corn/locate/advengsoft, Advances in Engineering Software 32 (2001) 49～60
206. Ray J. Paul and Tomas S. Chancy, Simulation optimisation using a genetic algorithm, Simulation Practice and Theory 6 (1998) 601～611
207. Lamia Djerid and Marie-Claude Portmarm, How to keep good schemata using cross-over operators for permutation problems,www.elsevier.com/locate/orms, Intl.Trans.in Op. Res.7 (2000) 637～651
208. Jasmina Arifovic, Evolutionary dynamics of currency substitution,Journal of Economic Dynamics & Control 25 (2001) 395～417
209. Fogel D B, A comparison of evolutionary programming and genetic algorithms on selected constrained optimizationproblems, Simulation, 1995, 64(6):397～406
210. Michalewicz Z etal, Evolutionary algorithms for constrained engineering problems, Computers Ind Engng, 1996, 30 (4) :851～870
211. J.G. NDIRITU, An Improved Genetic Algorithm for Rainfall-Runoff Model Calibration and F'unct ion Optimization, www.elssvier.nl/locate/mcm, Mathematical and Computer Modellii 33 (2001) 696～706
212. E.K. Kemsley, A hybrid classification method: discrete canonical variate analysis using a genetic algorithm, www.elsevier.com/locate/chemometics, Chemometrics and Intelligent Laboratory Systems 55 2001 39～51
213. Chan Ching Yuen, Aatmeeyata, Santosh K. Gupta and Ajay K. Ray, Multi-objective optimization of membrane separation modules using genetic algorithm, Journal of Membrane Science 176 (2000) 177～196
214. Antonio Brunetti, A fast and precise genetic algorithm for a non-linear fitting problem,www.elsevier.nl/Iocate/cpc, Computer Physics Communications 124 (2000) 204～211
215. Michael D. Vose and Jonathan E. Rowe, Random heuristic search: applications to GAs and functions of Unitation, www.elsevier.com/locate/cma, Comput.Methods Appi. Mech. Engrg.186 (2000)195～220
216. D. Greiner, G. Winter and J.M. Emperador, Optimising frame structures by different strategies of genetic algorithms, Finite Elements in Analysis an Design 37 (2001) 381～402
217. Cheng, R. and M. Gen, Genetic Algorithms for Multi-row Machine Layout Problem, Engineering Design and Automation, 1996
218．缪希仁，张培铭．多目标动态优化高级遗传算法及其在智能电磁电器设计中的应用研究．电工技术学报，1999，14(4)：27～30
219．钟守楠．遗传算法的收敛性与编码．武汉水利电力大学学报，2000，33(1)：108～112
220．陈国梁，王煦法，庄镇泉等．遗传算法及其应用．北京：人民邮电出版社，1996．
221．章珂，刘贵忠．交叉位置非等概率选取的遗传算法．信息与控制，1997，6(1)：53～60．
222．孙艳丰，王众托．自然数编码遗传算法的最优群体规模．信息与控制，1996，25(5)：317～320．
223．恽为民，席裕庚．遗传算法的运行机理分析．控制理论与应用，1996，13(3)：297～304．
224．袁宏，王兴华．具有Elitist选择的遗传算法的收敛速度估计．科学通报，1997，42(2)：144～147．
225．席裕庚，柴天佑，恽为民．遗传算法综述．控制理论与应用，1996，13(6)：697～708．
226．谢金星．进化计算简要综述．控制与决策，1997，12(1)：1～7．
227．丁承民，张传生，刘辉．遗传算法纵横谈．信息与控制，1997，26(1)：40～47．
228．孙艳丰，王众托．遗传算法在优化问题中的应用研究．控制与决策，1996，11(4)：425～431．
229．周金荣，黄道，蒋慰孙．遗传算法的改进及其应用研究．控制与决策，1995，10(3)：260～264．
230．黄炯，邬永革，李军等．基于遗传算法的系统在线辨识．信息与控制，1996，25(3)：171～176．
231．田奕，刘涛，李国杰．求解可满足性问题的一种高效遗传算法．模式识别与人工智能，1996．9(3)：209～212
232．朱鳌鑫·遗传算法的适应度函数研究·系统工程与电子技术，1998，(11)：58～62
233．刘勇，康立山，陈毓屏．非数值并行算法(第二册)遗传算法．科学出版社，1995
234．恽为民，席裕庚．遗传算法综述．控制理论与应用，1996，13(6)：697～708
235．张良杰，毛志宏，李衍达．遗传算法中突变算子的数学分析及改进策略．电子科学学刊，1996，18(6)：590～595
236．李逍波，胡庆生，林争辉·一种适用于优化分配问题的对称遗传算法．计算机辅助设计与图形学报，1998，Vol．10,No．1,Jan．，22～28
237．陈建安，郭大伟等．遗传算法理论研究综述．西安电子科技大学学报，1998，Vol．25，No．3，Jun．363～367
238．刘曙光，费佩燕，侯志敏．生物进化论与人工智能中的遗传算法．自然辩证法研究，1999，15(12)：20～24
239．王大明，毛宗源．并行算法综述．济南大学学报(自然科学版)，1998，19(1)：20～25
240．辛，朱鳌鑫．一种求解复杂函数最优解的遗传算法．计算机工程与设计，1998，19(5)：52～58
241．张帆，唐湘蓉．基于遗传算法的优化搜索技术．矿物岩石，1998，增刊：236～238
242．隋洪涛，陈红全．多目标翼型优化设计基因算法研究．空气动力学学报，2000，18(2)：236～240
243．唐亮，刘军，许晓鸣．面向对象的遗传算法工具包及在函数优化中的应用．计算技术与自动化，1999，18(2)：6～9
244．童彤，丰镇平，李军．遗传算法在透平叶栅多目标优化设计中的应用．中国电机工程学报，1999，19(6)：74～76
245．王国庆，郭振邦等．基于改进遗传算法的离散变量结构可靠性优化设计．天津大学学报，1998，31(5)：569～575
246．王英勋，陈宗基．基于遗传算法(GA)的具有约束的飞行轨迹规划．北京航空航天大学，1999，25(3)：355～358
247．王宏刚，曾建潮，徐玉斌．优良模式自学习遗传算法．自动化学报，1999，25(3)：375～379
248．隋洪涛，陈红全，唐智礼．基因算法在喷管反设计中的应用．南京航空航天大学学报，1999，31(2)：127～132．
249．雷德明．自调整遗传算法．系统工程与电子，1999，21(11)：70～71
250．李凡，徐章艳．遗传算法种群多样性的度量．华中理工大学学报，1999，27(7)
251．林丹，寇纪淞，李敏强．遗传规划研究与应用中的若干问题．管理科学学报，2(4)：62～69
252．许海平，张彤，王子才．浮点数编码遗传算法及其在电站机组组合优化种的应用·小型微型计算机系统，1999，20(8)：578～572
253．王熙法．遗传算法及其应用．小型微型计算机系统，1995，16(2)：59～64
254．韩明华，袁乃昌．基于整体退火遗传算法的不等间距天线阵的综合．系统工程与电子技术，1999，21(2)：70～74
255．蒋培，黄炎，杨敬安．关于遗传算法收敛性的分析．湖南师范大学自然科学学报，1999，22(1)：14～18
256．吴少岩，张青富，陈火旺．基于家族优生学的进化算法．软件学报，1997，(2)：137～144
257．张晓馈，戴冠中，徐乃平．遗传算法种群多样性的分析研究．控制理论与应用，1998，15(1)：17～23
258．李海民，吴成柯．遗传算法性能与所求解问题关系的研究．西安电子科技大学学报，1999，26(6)：752～757
259．马丰宁，寇纪淞，李敏强．遗传算法中的满意度与最优距．系统工程理论与实践，1998
(1)：18～21
260．隋洪涛，陈红全．基于B样条的气动反设计遗传算法研究．南京航空航天大学学报，1999，31(1)：18～23
261．刘洪杰，王秀峰，王治宝．利用遗传算法搜索多个极值点．南开大学学报，2000，33(3)：17～22
262．周明，孙树栋，彭炎午．基于遗传模拟退火算法的机器人路径规划．航空学报，1998，19(1)：118～120
263．王宏刚 曾建潮．基于Metropolis判别准则的遗传算法．控制与决策，1998，13(2)：181～184
264．殷铭，张兴华，戴先中．基于MATLAB的遗传算法实现．计算机应用，2000(1)：9～11
265．霍红卫，许进，保铮．选择与变异算子的作用分析．电子学报，2000，28(2)：31～48
266．佘春峰等·浮点遗传算法的收敛性及其在模型参数提取问题中的应用·电子学报，2000，28(3)：134～136
267．雷德明．多维实数编码遗传算法．控制与决策，2000，15(2)：239～241
268．黄昱坤，韩生廉，胡国四．遗传算法非效率操作的改进方法．控制与决策，2000，15(2)：251～253
269．郑金华，蔡自兴．遗传算法中的模式及其转换初探．计算机工程与应用，2000(4)：42～44
270．周琳霞，杨小琴，黎明．动态变异率的多群体进化计算．南昌航空工业学院学报，2000，14(1)：84～86
271．任庆生，叶中行，曾进．基于实数型遗传算法的电子系统可靠性最优分配．通信学报，2000，21(3)：43～46
272．戴晓晖，利敏强，寇纪淞．遗传算法理论研究综述．控制与决策，2000，15(3)：263～273
273．孙瑞祥，屈梁生．遗传算法优化效率的定量评价．自动化学报，2000，26(7)：552～556
274．王健，王建华．标准遗传算法的研究进展．华东船舶工业学院学报，2000，14(3)：29～34
275．钟求喜，谢涛，陈火旺．遗传算法中解个体的生存策略．计算机工程与科学，2000，22(1)：14～17
276．徐进雯，李元春．一种改进编码策略在演化函数优化中的应用．计算机工程与应用，2000，2：49～53
277．汪俐，张铃．用网络实现交叉操作的遗传算法．计算机工程与科学，2000，22(1)：18～20
278．郑金华，陈静，蔡自兴．主从式控制网络并行GA的设计与实现．计算机工程与科学，2000，22(1)：21～24
279．孟祥武，张玉洁．遗传算法交叉操作的可达性．计算机工程与科学，2000，22(1)：25～27
280．孙力娟，吴新余．采用新杂交运算的遗传算法在求解优化问题中的应用．南京邮电学院学报，1997，17(1)：94～97
281．陈海峰等．利用改进遗传算法实现图像特征点的匹配．南京大学学报，2000，36(2)：171～176
282．王健，李露．基于遗传算法的机械方案设计系统的研究．基础自动化，2000，7(2)：57～59
283．基于遗传算法的预测自整定PID控制器．控制与决策，2000，15(1)：113～118
284．赵正佳，黄洪钟，陈新．优化设计求解的遗传—神经网络新算法研究．西南交通大学学报，2000，35(1)：65～68
285．周济等．面向对象的遗传算法及其在神经网络辅助设计中的应用．计算机工程与应用，2000，2：54～56
286．郝翔，李人厚．适用于复杂函数优化的多群体遗传算法．控制与决策，1998，13(3)：263～266
287．马丰宁．遗传算法与遗传规划运行机理的研究．天津大学博士学位论文，1998
288．张铃，张钹．统计遗传算法．软件学报，1997，8(5)：335～344
289. Grefenstette J J, Conditions for implicit parallelism, In:Foundations of Genetic Algorithms. CA:Morgan Kaufmann, 1991:252～261
290. Muhlenbein H. , Evolution in time and space——The parallel genetic algorithm, In:Foundations of Genetic Algorithms. CA:Morgan Kaufmann, 1991:316～337
291. Radcliffe N J, Equivalence class analysis of genetic algorithms, Complex Systems, 1991,5(2):183～205
292. Radcliffe N J, Non-linear genetic representations, ln: Parallel Problem Solving from Nature, Am sterdam: Elsevier Science, 1992:259～268
293. Vose M D and Liepins G E. Schema disruption. In:Proc of the4th Int Conf on Genetic Algorithms. CA:Morgan Kaufm ann, 1991:237～242
294．林于一，沈阳．遗传算法在非线性约束优化问题上的应用．机械设计与研究，1998(2)：13～15
295．李立，李柏林，陈永．BUCHBERGER算法及其在优化设计中的应用．机械设计与研究．1996，(1)：2～4
296．陈国良，王煦法，庄镇泉等．遗传算法及其应用．北京：人民邮电出版社，1996