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基于免疫B-Cell算法求解可满足性问题的性能分析

Performance Analysis of Immune Inspired B-Cell Algorithm for the SAT Problem
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摘要 可满足性问题(SAT)是计算机科学和人工智能研究中的核心NP-完全问题.构造了两类SAT问题实例,易解和难解实例.从理论上分析了B-Cell算法求解该两个实例的运行时间,并证实了B-Cell算法在某些问题上有效而在一些问题上无效.进一步提出了一个简单的基于免疫的多目标优化算法(IBMO),对于一个双目标的SAT问题,证明了IBMO能够有效地找到整个Pareto前沿.这些分析结果从理论上证实和说明了人工免疫系统的有效性. The satisfiability problem is a basic core NP-complete problem in computer science and artificial intelligence.We construct two classes of SAT instances,and analyze the runtime of the B-Cell algorithm for these two instances.We proved that there exist situations where the BCA is efficient or inefficient.On the other hand,we develop a simple immune-based multi-objective optimizer(IBMO)and reveal that IBMO can find the whole Pareto front for a bi-objective sat problem in expected polynomial runtime.These analysis results exemplify and strengthen the usefulness of artificial immune systems from a theoretical perspective.
作者 夏小云 周育人 XIA Xiao-yun ,ZHOU Yu-ren (College of Mathematics Physics and Information Engineering, Jiaxing University, Jiaxing 314001, China; 2 School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China; 3 School of Data and Computer Science, Sun Yat-sen University, Guangzhou 510006, China)
出处 《微电子学与计算机》 CSCD 北大核心 2016年第7期5-10,共6页 Microelectronics & Computer
基金 国家自然科学基金项目(61170081,61472143) 江西省自然科学基金(20151BAB217008)
关键词 人工免疫系统 B-Cell算法 多目标优化 可满足性问题 运行时间分析 artificial immune system(AIS) B-Cell algorithm multi-objective optimization satisfiability problem runtime analysis
作者简介 夏小云男,(1982-),博士,讲师.研究方向为计算智能与机器学习.E-mail:scutxxy@gmail.com. 周育人男,(1965-),博士,教授,博士生导师.研究方向为计算智能、数据挖掘及社会网络.
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  • 1Garey M R, Johnson D S. Computers and intractabili- ty-a guide to the theory of NP-completeness[M]. New York, Freeman, 1979. 被引量:1
  • 2Cook S. The complexity of theorem-proving proce- dures[C]//Proc of the 3rd Ann. ACM Symp. on Theo- ry of Computing, Assoc. Comput. Mach. New York, 1971 : 151. 被引量:1
  • 3Beame P, Kautz H, Sabharwa A. Towards under- standing and harnessing the potential of clause learning [J]. Journal of Artificial Intelligence Research, 2004,22(2): 319-335. 被引量:1
  • 4Hlirsch E A, Kojevnikov a. UnitWalk. A new SAT solver that uses local search guided by unit clause elim- ination[J]. Annals of Mathematics and Artificial Intel- ligence, 2005, 43(1): 91-111. 被引量:1
  • 5Dasgupta D, NIfiO L F. Immunological computation: theory and applications IMp. Baca Raton:CRC Press, 2008. 被引量:1
  • 6Kelsey J, Timmis J. Immune inspired somatic contigu- ous hypermutation for function optimization[C]//Ge- netic and Evolutionary Computation Conference. Hei- delberg, Springer, 2003: 207-218. 被引量:1
  • 7Jansen T, Zarges C. A theoretical analysis of immune inspired somatic contiguous hypermutations for func- tion optimization[C]//Proc of the International Con- ference on Artificial Immune Systems, ICARIS. Ber- lin, Springer, 2009: 80-94. 被引量:1
  • 8Jansen T, Oliveto P S, Zarges C. On the analysis of the immune-Inspired B-Cell algorithm for the vertex cover problem[C]//ICARIS 2011. Springer, Heidel- berg, 2011: 117-131. 被引量:1
  • 9Jansen T, Zarges C. Computing longest common sub- sequences with the B-Cell algorithm[C]//Proc of the llth International Conference on Artificial Immune Systems ( ICARIS ' 12 ). Italy, Taormina, 2012: 111-124. 被引量:1
  • 10Wegener I. Methods for the analysis of evolutionary algorithms on pseudo-Boolean functions[C]//In Evolu- tionary Optimization. Norwell, USA, Kluwer Academ- ic Publishers, 2002: 349-369. 被引量:1
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