邻域形态空间多源免疫检测器生成与检测

人工免疫系统（artificial immune system，简称AIS）是人工智能技术的重要分支之一，被广泛应用于异常检测、数据挖掘、机器学习等多个领域.检测器是其核心知识集，其生成、优化和检测操作决定了人工免疫的应用效果.目前，人工免疫的问题空间以实值形态空间为主，但实值非自体空间“黑洞”、检测器生成速率慢、检测器高重叠冗余、“维度灾难”等问题，使得人工免疫检测的效果不甚理想.鉴于此，使用邻域形态空间，并改进邻域否定选择算法（neighborhood negative selection algorithm，简称NNSA），引入混沌理论和遗传算法，提出了一种多源邻域否定选择算法（multi-source-inspired NNSA，简称MSNNSA），并基于此提出邻域形态空间多源免疫检测器生成与检测方法，改进邻域形态空间下检测器的构造与生成机制，使其更具靶向性，并使获得的检测器具有更好的分布性，提高其生成效率和整体的检测性能，解决以上实值形态空间下存在的问题.实验结果表明，该方法提高了检测器生成效率以及检测的整体性能和稳定性.

Multi-source-inspired Immune Detector Generation and Detection in Neighborhood Shape-space

Artificial immune system (AIS) is one of the important branches of artificial intelligence technology, and it is widely used in many fields such as anomaly detection, data mining, and machine learning. The detectors are its core knowledge set, and the application effects are determined by the generation, optimization, and detection of the detectors. At present, the problem space of AIS mainly applied real-valued shape-space. But the detectors in the real-valued shape-space have some problems that have not been solved, such as the holes in the non-self-shape-space, slow speed of generation, detector overlapping redundancy, dimension curse, which lead to the unsatisfactory detection effects. In view of this, based on the neighborhood shape-space, a new shape-space, and the improved neighborhood negative selection algorithm, a multi-source-inspired neighborhood negative selection algorithm (MSNNSA) is proposed by introducing chaotic map and genetic algorithm. And then, based on this algorithm, the multi-source-inspired immune detector generation and detection methods in neighborhood shape-space are built to make the construction and generation more targeted, so that the generated detectors have better distribution performance. Meanwhile, the method also improves the detectors'' generation efficiency and the detection performances, and overcomes the shortcomings in the real-valued shape-space mentioned before. Experimental results show that the proposed method enhances generation efficiency, whole detection performances, and stability.