期刊文献+

基于柯西变异的免疫单克隆策略 被引量:9

Immune Monoclonal strategy based on the Cauthy mutation
下载PDF
导出
摘要 系统地阐述了基于细胞克隆选择学说的克隆算子,将其应用于进化策略,并利用柯西变异替代传统进化策略中的高斯变异,提出了改进的进化策略算法———基于柯西变异的免疫单克隆策略算法,并利用Markov链的有关性质,证明了该算法的收敛性.理论分析和仿真实验表明,与传统的进化策略算法以及免疫克隆算法相比,基于柯西变异的免疫单克隆策略算法不仅有效克服了早熟问题、保持了解的多样性,而且收敛速度比前两者都快. Based on the clonal selection theory, the main mechanisms of clone are analyzed. An improved evolutionary strategy algorithm - Immune Monoclonal Strategy algorithm based on the Cauthy Mutation (IMCSCM) is presented, in which the Gauss mutation in the Classical Evolutionary Strategies algorithm (CES) is replaced by the Cauthy one. Compared with CES and the Immune Monoclonal Strategy algorithm applying the Gauss Mutation (IMCSCM), IMCSCM is shown to be an evolutionary strategy capable of avoiding prematurity, increasing the converging speed and keeping the variety of solution in the simulations. Using the theories of Markov Chain, its convergence is proved.
出处 《西安电子科技大学学报》 EI CAS CSCD 北大核心 2004年第4期551-556,共6页 Journal of Xidian University
基金 国家自然科学基金资助项目(60133010)
关键词 克隆选择 进化算法 进化策略 MARKOV链 柯西变异 Computer simulation Convergence of numerical methods Optimization
  • 相关文献

参考文献13

  • 1Schwefel H P, Mnner R. On Parallel Problem Solving from Nature[A]. Lecture Notes in Computer Science, Proc of 1st Int1 Conf[C]. Berlin: Springer-Verlag, 1991.
  • 2陆德源.现代免疫学[M].上海:上海科学技术出版社,1998.14-16.
  • 3Fogel D B, Atmar J W. Comparing Genetic Operators with Gaussian Mutations in Simulated Evolutionary Processing Using Linear Systems[J]. Biological Cybernetics, 1993, 63(2): 111-114.
  • 4Schwefel H P. Evolutionary Optimum Seeking[M]. New York: John Wiley&Son, 1995.
  • 5Szu H H, Hartley R L. Nonconvex Optimization by Fast Simulated Annealing[J]. Proceeding of IEEE, 1987, 75(3): 1538-1540.
  • 6Kappler C. Are Evolutionary Algorithms Improved by Larger Mutations?[A]. In Parallel Problem Solving from Nature (PPSN) Ⅳ[C]. Berlin: Springer-Veralg, 1996. 346-355.
  • 7Yao X. A New Simulated Annealing Algorithms[J]. Int J of Computer Math, 1995, 56(1): 162-168.
  • 8Yao X, Liu Y. Fast Evolutionary Programming[A]. Proc of the Fifth Annual Conference on Evolutionary Programming[C]. Cambridge: MIT Press, 1996. 451-461.
  • 9Yao X, Lin G, Liu Y. An Analysis of Evolutionary Algorithms Based on Neighbourhood and Step Sizes[A]. Proc of the Sixth Annual Conference on Evolutionary Programming[C]. Berlin: Springer-Veralg, 1997. 297-307.
  • 10潘正军 康立山.演化计算[M].北京:清华大学出版社,1998..

共引文献23

同被引文献59

  • 1HaiyanPan,JunZhu,DanfuHan.Genetic Algorithms Applied to Multi-Class Clustering for Gene Ex-pression Data[J].Genomics, Proteomics & Bioinformatics,2003,1(4):279-287. 被引量:9
  • 2李洁,高新波,焦李成.一种基于CSA的模糊聚类新算法[J].电子与信息学报,2005,27(2):302-305. 被引量:2
  • 3GONG Maoguo,DU Haifeng,JIAO Licheng.Optimal approximation of linear systems by artificial immune response[J].Science in China(Series F),2006,49(1):63-79. 被引量:21
  • 4戎嘉余,詹仁斌.论大灭绝后的幸存类型、复活效应与避难所[J].地学前缘,2006,13(6):187-198. 被引量:13
  • 5Dirk Thierens. Adaptive mutation rate control schemes in genetic algorithms [ J]. Inst. of lnf & Comput, Sci. , 2002, 980 - 985.
  • 6Eiben A E, et al. Global convergence of genetic algorithm: putation an infinite markov chain analysis [C]. In: Schwefel H P,Manner R Eds. Parallel Problem Solving from Nature. Heidelberg, Berlin: Springer-verlag, 1991, 4 - 12.
  • 7T. Back, M. Schutz. Intelligent Mutation Rate Control in Canonical Genetic Algorithms [ C ]. Proc. of the Interna- tional Symposium on Methodologies for Intelligent Systems, 1996: 158 - 167.
  • 8J. J. Grefenstette, J. E. Baker. How genetic algorithms work a critical look at implicit parallelism [ C !. In Proc. Third Int. Conf on Genetic Algorithms. San Mateo CA Morgan Kaufmann, 1989:20 -27.
  • 9Ting Kuo, Shu-Yuen Hwang. A Genetic Algorithm with Disruptive Selection [ J]. IEEE Transactions on Systems, Man, and Cybernetics--Part B: Cybernetic, 1996, 26 (2), 299 - 307.
  • 10M. Srinivas, L. M. Patnaik. Adaptive Probabilities of Crossover Genetic in Mutation and Algorithms [ J ]. IEEE Transactions on Systems, Man and Cyberntics, 1994, 24 (4) : 656 - 667.

引证文献9

二级引证文献22

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部