A Novel Approach of Detector Generation for Real-Valued Negative Selection Algorithm

摘要：

　　The detector sets generated by Real-Valued Negative Selection Algorithm (RNSA) are usually numerous,without optimization,and can not work under real-time condition.Thus,a novel approach of detector generation for RNSA based on Clonal Selection and Neighborhood Search (CSNS-RNSA) is proposed.Clonal selection of the immune mechanism is introduced to implement global search in a quasi-random sequence.The Gaussian mutation operator is proposed to get the global optimal detection sets of N-dimensional space through Neighborhood search.The resulting detector sets achieved a good coverage of non-self space,and also significantly reduced the number of detector sets,thus overcome the limitations of original RNSA.Finally,experiments verify the effectiveness of the algorithm.

关键词： Negative Selection Algorithm Detector Generation Clonal Selection Neighborhood Search

作者: Ronghua Hu Peihuang Lou Peng Zhao

作者单位: College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics andAstronautics, College of Mechanical and Electrical Engineering, Nanjing University of Aeronautics andAstronautics,

会议类型: 国际会议

会议名称: the Second International Conference on Frontiers of Manufacturing and Design Science(第二届制造与设计科学国际会议(ICFMD 2011))

会议地点: 台湾

会议语种:英文

页码: 3736-3740

在线出版日期: 2011-12-11（万方平台首次上网日期，不代表论文的发表时间）