Liu Ruochen

A noval algorithm of artificial immune system for high-dimensional function numerical optimization∗

Abstract Based on the clonal selection theory and immune memory theory, a novel artificial immune system algorithm, immune memory clonal programming algorithm (IMCPA), is put forward. Using the theorem of Markov chain, it is proved that IMPCA is convergent. Compared with some other evolutionary programming algorithms (like Breeder genetic algorithm), IMPCA is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like high-dimensional function optimization, which maintains the diversity of the population and avoids prematurity to some extent, and has a higher convergence speed. ∗Supported by National Natural Science Foundation of China (Grant Nos. 60133010 and 60372045)

Immunity clonal strategies

Based on the clonal selection theory, the main mechanisms of clone, which will be explored in the field of artificial intelligence, are analyzed in this paper. An improved evolutionary strategy algorithm, immunity clonal strategy algorithm (ICS), which includes immunity monoclonal strategy algorithm (IMSA) and immunity polyclonal strategy algorithm (IPSA), is put forward. Compared with the classical evolutionary strategy algorithm (CES), ICS is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like multi-objective optimization, and the results are better. Using the theories of Markov chain, it is proved that ICS algorithm is convergent.

Adaptive chaos clonal evolutionary programming algorithm

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.

Adaptive polyclonal programming algorithm with applications

Based on the clonal selection theory, the main mechanism of immune clone applied in artificial intelligence is analyzed in this paper. A new operator, adaptive polyclonal operator as well as a novel artificial immune system algorithm, APPA (adaptive polyclonal programming algorithm), is put forward. Compared with some other evolutionary programming algorithms (like breeder genetic algorithm), APPA, behaving as an evolutionary strategy, is shown to be capable of solving complex machine learning tasks effectively, like multimodal function optimization.