期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于文化微粒群优化算法的DNA编码研究 被引量：1

Research on DNA Encoding Based on Cultural Particle Swarm Optimization Algorithm
下载PDF
导出
摘要 对DNA编码约束进行研究,选择汉明测量以及相似度作为DNA序列集设计的主要约束,并结合连续性约束与GC Content约束,将序列集设计问题抽象为带有强约束的多目标优化问题,采用文化微粒群算法解决该多目标优化问题。仿真结果表明,该混合算法针对DNA编码序列设计问题,在求解最优值能力、解的稳定性方面都能取得较好的效果。 DNA encoding constrained is researched. H-measure and similarity is the principal constrained for DNA sequence design. Continuity and GC Content is also another constrained. DNA sequence design is presented to solve the multi-objective optimization problem. Particle Swarm Optimization based on Cultural Algorithm（PSO-CA） is proposed to solve the DNA sequence design as a multi-objective optimization problem. Simulation results indicate the hybrid algorithm does well on searching efficiency and key stability for DNA sequence design problem.
作者 殷脂 叶春明 温蜜
机构地区 上海理工大学管理学院 上海电力学院计算机信息工程学院
出处 《计算机工程》 CAS CSCD 北大核心 2011年第3期10-12,共3页 Computer Engineering
基金 国家自然科学基金资助项目(60903188) 上海市高校选拔培养优秀青年教师科研专项基金资助项目(sdl-07013) 高等学校博士点基金资助项目(20093120110008) 上海市重点学科建设基金资助项目(S30504)
关键词 微粒群优化算法 文化演化 DNA编码 汉明测量 Particle Swarm Optimization（PSO） algorithm cultural evolution DNA encoding H-measure
分类号 TP301 [自动化与计算机技术—计算机系统结构]
  • 相关文献

参考文献5

  • 1Adleman L.Molecular Computation of Solution to Combinatorial Problems[J].Science,1994,66(11):1021-1024.
  • 2Deaton R,Murphy R C,Garzon M,et al.Good Encodings for DNA-based Solutions to Combinatorial Problems[C]//Proceedings of the 2nd DIMACS Workshop on DNA-Based Computers.[S.1.]:IEEE Press,1996.
  • 3Shin S Y,Lee I H,Kim D.Multiobjective Evolutionary Optimization of DNA Sequences for Reliable DNA Computing[J].IEEE Transactions on Evolutionary Computation,2005,9(2):143-158.
  • 4马翠,周先东,杨大地.分层粒子群优化算法[J].计算机工程,2009,35(20):194-196. 被引量：10
  • 5马慧民,叶春明.基于文化进化的并行粒子群算法[J].计算机工程,2008,34(2):193-195. 被引量：12

二级参考文献14

  • 1马慧民,柳毅,叶春明.基于改进粒子群算法求解单级多资源约束生产批量计划问题[J].工业工程与管理,2005,10(6):66-70. 被引量：26
  • 2马慧民,叶春明.粒子群算法在贷款组合优化决策中的应用[J].计算机工程与应用,2006,42(14):219-221. 被引量：9
  • 3马慧民,叶春明,柳毅.基于改进粒子群算法的生产批量计划问题研究[J].计算机集成制造系统,2006,12(9):1417-1420. 被引量：21
  • 4冯林,颜世鹏,孙焘.邻域搜索的粒子群优化算法及其性能分析[J].计算机工程与科学,2006,28(12):72-73. 被引量：2
  • 5Shi Y, Eberhar R. A Modified Particle Swarm Optimizer[C]//Proc. of the IEEE International Conference on Evolutionary Computation. Piscataway, NJ, USA: IEEE Press, 1998.
  • 6Eberhard R, Kennedy J. A New Optimizer Using Particle Swarm Theory[C]//Proceedins of the 6th International Symposium on Micro Machine and Human Science. Piscataway, NJ, USA: [s. n.], 1995.
  • 7Kennedy J, Eberhard R. Particle Swarm Optimization[C]// Proceedins of IEEE Int'l Conference on Neural Networks. Piscataway, NJ, USA: [s. n.], 1995.
  • 8Reynolds R G An Introduction to Cultural Algorithms[C]// Proceedings of the 3rd Annual Conference on Evolutionary Programming. San Diego, California: [s. n.], 1994.
  • 9艾景波,腾弘飞.文化粒子群优化算法[Z].(2006-12-10).http://www.paper.edu.cn.
  • 10Reynolds R G, Michalewicz Z, Cavaretta M. Using Cultural Algorithms for Constraint Handling in GENOCOP[C]//Proceedings of the 4th Annual Conference on Evolutionary Programming. Cambridge, Massachusetts: MIT Press, 1995: 298-305.

共引文献20

同被引文献25

  • 1王国仁,葛健,徐恒宇,郑若石.基于二分频率变换的序列相似性查询处理技术[J].软件学报,2006,17(2):232-241. 被引量：8
  • 2CHAUDHURI P, DAS S. SWORDS: a statistical tool for analyzing large DNA sequences[J]. Journal of Biosciences, 2002, 27 (1) : 1-6.
  • 3CHAUDHURI P, DAS S. Statistical analysis of large DNA sequences using distribution of DNA words - J ]. Current Science, 2001,80(9) : 1 161-1 166.
  • 4PORIKLI FM. Clustering variable length sequences by eigen- vector decomposition using HMM [C]// Prec. of the int'l workshop on Structural and Syntactic Pattern Recognition. LNCS 3138, London: Springer-Verlag, 2004 : 352-360.
  • 5SRIKANT R, AGRAWAL R. Mining sequential patterns: Generalization and performance improvements[ C] //Proc. of the 15th Int'l Conf. on Extending Database Technology. London: Springer-Verlag, 1996 : 3-17.
  • 6PEI J, HAN J W, MORTAZAVI-ASL B, et al. Prefixspan: mining sequential patterns efficiently by prefix-projected growth[C] // Proc. of the 17th Int'l Conf. on Data Engineering. Washington: IEEE Computer Society, 2001: 215-224.
  • 7HAN J, PEI J, MORTAZAVI-ASL B, et al. Freespan: hequent pattern-projected sequential pattem mining [ C ] // Proc. of the 6th ACM SIGKDD Int'l Conf. on Knowledge Discovery in Databases. New York: ACM, 2000: 355-359.
  • 8ZAKI M J. SPADE: an efficient algorithm for mining frequent sequences[J]. Machine Learning, 2001,42(1-2) : 31-60.
  • 9ESTER M, ZHANG X. A top-down method for mining most specific frequent pattems in biological sequence data[C] // Proc. of the 4th SIAM Int'l Conf. on Data Mining, 2004: 90-101.
  • 10WANG K, XU Y, JEFFREY X Y. Sealable sequential pattern mining for biological sequences[ C]//Proc. of the 13th ACM Int'l Conf. on Information and Knowledge Management. New York: ACM Press, 2004: 178-187.

引证文献1

计算机工程
2011年 第3期

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