Abstract: Clonal selection algorithm is the core of the detectors’evolution in immune invasion theory. In traditional immune clonal selection algorithm, it is difficult to take into account the global and local search with a single mutation, which leads to the disadvantage of easy to fall into local optimization or slow convergence. By introducing cultural algorithm, this paper realizes the evolution of population space and belief space. This paper proposes clonal selection algorithm of adaptive hybrid mutation, which combines the strong global search ability of cauchy mutation with the strong local search ability of chaos mutation in the mutation. And it utilizes the knowledge of belief space to adaptively determine the time and the proportion of the two kinds of mutations. The algorithm is tested in KDDCUP99 data set. The result shows that the algorithm has good convergence and robustness.

Key words: immune intrusion detection, cultural algorithm, cauchy mutation, chaos mutation, clonal selection

摘要： 克隆选择算法是免疫入侵理论中检测器进化的核心。传统免疫克隆选择算法中通过单一的变异很难同时兼顾全局和局部搜索，从而导致容易陷入局部最优或者收敛速度慢等弊端，通过引入文化算法，实现种群空间和信仰空间双层进化，在变异时将全局搜索能力强的柯西变异和局部搜索能力强的混沌变异相结合，提出了自适应混合变异克隆选择算法，利用信仰空间的知识来自适应地确定两种变异的作用时间和作用比例，通过KDDCUP99数据集进行测试，结果显示该算法有较好的收敛性和鲁棒性。

关键词: 免疫入侵检测, 文化算法, 柯西变异, 混沌变异, 克隆选择