Based on the chaos movement and the clonal selection theory, a novel artificial immune system algorithm, Adaptive Chaos Clonal Evolutionary Programming Algorithm （ACCEP）, is proposed in this paper. The new algorithm uses the Logistic Sequence to control the mutation scale and uses the Chaos Mutation Operator to control the clonal selection. Compared with SGA and Clonal Selection Algorithm, ACCEP can enhance the precision and stability, avoid prematurity to some extent, and have the high convergence speed. The results of the experiment indicate that ACCEP has the capability to solve complex machine learning tasks, like Multimodal Function Optimization.
This paper puts forward a novel artificial immune response algorithm for optimal approximation of linear systems. A quaternion model of artificial immune response is proposed for engineering computing. The model abstracts four elements, namely, antigen, antibody, reaction rules among antibodies, and driving algorithm describing how the rules are applied to antibodies, to simulate the process of immune response. Some reaction rules including clonal selection rules, immunological memory rules and immune regulation rules are introduced. Using the theorem of Markov chain, it is proofed that the new model is convergent. The experimental study on the optimal approximation of a stable linear system and an unstable one show that the approximate models searched by the new model have better performance indices than those obtained by some existing algorithms including the differential evolution algorithm and the multi-agent genetic algorithm.
本文提出了一种新的数据挖掘分类方法——免疫克隆分类算法（Immune Clonal Algorithm for Classification，ICAC）．ICAC是一种基于免疫克隆算法的搜索机制和Michigan方法模型的规则提取和分类方法．与遗传分类算法不同，ICAC是一种自下而上的分类算法．ICAC虽然着眼于规则的进化，但是从编码到免疫算子的设计都立足于训练样本，可避免进化过程中产生无意义规则，且产生的规则是可解释的．文中将算法用于UCI数据集，并与现有的基于非遗传算法、遗传算法和分布式遗传算法的分类方法进行了比较实验．结果表明，ICAC是一种有效的分类算法．
