计算机科学 ›› 2012, Vol. 39 ›› Issue (8): 246-251.

• 人工智能 • 上一篇    下一篇

一种基于受体编辑的实值阴性选择算法

李贵洋,郭 涛   

  1. (可视化计算与虚拟现实四川省重点实验室 成都610066);(四川师范大学计算机科学学院 成都610101)
  • 出版日期:2018-11-16 发布日期:2018-11-16

Receptor Editing-inspired Real Negative Selection Algorithm

  • Online:2018-11-16 Published:2018-11-16

摘要: 受生物免疫受体编辑理论的启发,提出了一种基于受体编辑的实值阴性选择算法RERNS(Receptor Editing-inspired Real Negative Selection Algorithm)。对于匹配自体的检测器,该算法采用定向受体编辑使之获得新生,而这些新生的检测器分布在自体与非自体的边界区域,从而增加了检测器的多样性,并改善了算法对边界区域的覆盖情况;对于不匹配自体的检测器,该算法采用识别相同最近自体的定向受体编辑,使检测器在包含原检测范围的情况下扩大了对非自体空间的覆盖。理论分析和实验验证表明,与实值阴性选择算法中具有代表性的RNS算法和V-detector算法相比,RERNS算法生成的未成熟检测器更少,且检测性能更好。

关键词: 人工免疫系统,阴性选择算法,受体编辑

Abstract: Inspired by theory of biological immune receptor editing,a receptor editing-inspired real negative selection algorithm(RERNS) was proposed. For the detector that matches self,algorithm uses directional receptor editing to make a new life. These new detectors are located in the area of self and non-self boundary, thereby the diversity of detector is increased and the boundary covered by the algorithm is also improved. For the detector that does not match self , algorithm uses direction receptor editing for identifying identical nearest self to expand coverage of no-self space under the circumstances of containing original scope of detector. Theoretical analysis and experimental verification show that RERNS algorithm generates less un-mature detectors and obtains better detection performance than the most representative RNS algorithm and V-detector algorithm.

Key words: Artificial immune system,Negative selection algorithm,Receptor editing

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