计算机应用研究2017，Vol.34Issue(5)：1293-1297,5.DOI:10.3969/j.issn.1001-3695.2017.05.003
免疫进化否定选择算法
Immune evolution negative selection algorithm
摘要
Abstract
When the samples distribute densely,the traditional negative selection algorithm is difficult to generate detectors in the gap between normal and abnormal samples,it causes that the algorithm has the low detecting rate for these samples.In order to enable the detector to effectively identify the densely samples,this paper proposed the immune evolution negative selection algorithm (IENSA).By adding two immune evolution processes,IENSA could generate detector in the gap between normal and abnormal samples effectively,and restrain the redundant detector in the sparse area of the sample distribution.The experimental result show that,on the artificial data set Rectangle (2D) and the UCI standard data set Skin segmentation (3D),compared to the classical RNSA and V-detector algorithm,IENSA can reach the higher detection rate with the less antibodies and training time.关键词
人工免疫/否定选择算法/检测器/免疫进化Key words
artificial immune/negative selection algorithm/detector/immune evolution分类
信息技术与安全科学引用本文复制引用
高江锦,杨韬..免疫进化否定选择算法[J].计算机应用研究,2017,34(5):1293-1297,5.基金项目
国家自然科学基金资助项目(61402308) （61402308）
四川省教育厅自然科学重点资助项目(15ZA0146,15ZB0142) （15ZA0146,15ZB0142）
