基于混沌理论的否定选择算法

为了提高人工免疫系统中抗体生成速度，基于免疫系统的混沌特征，提出了混沌否定选择算法。该算法首先利用混沌的遍历性产生候选检测器集合，然后利用混沌扰动直接产生较优的个体，缩小搜索空间，并利用混沌初值敏感的特性，克服冗余个体。实验表明，该算法不但更好地保持了种群多样性，而且有效地提高了检测器集的生成速度。

A Negative Selection Algorithm Based on Chaos Theory

To increase antibody generating speed in artificial immune system, a chaos negative selection algorithm was presented which is based on the chaotic character of immune system. Over-spread character of chaos sequence was used to generate candidate set of detector firstly, and then the chaos disturbance was used to get some individuals directly and reduce search space, additionally chaos initial value sensitivity was used to overcome redundancies. The experimental resuits showed that the algorithm noy only retains the diversity of population but also increases the generating efficiency of detector.