Abstract
Based on Clonal Selection Theory, Parallel Chaos Immune Evolutionary Programming (PCIEP) is presented. On the grounds of antigen-antibody affinity, the original antibody population can be divided into two subgroups. Correspondingly, two new immune operators, Chaotic Clonal Operator (CCO) and Super Mutation Operator (SMO), are proposed. The former has strong search ability in the small space while the latter has better search ability in large space. Thus, combination searching local optimum with maintaining population diversity can be actualized by concurrently operating CCO and SMO. Compared with the Classical Evolutionary Programming (CEP) and Evolutionary Algorithms with Chaotic Mutations (EACM), experimental results show that PCIEP is of high efficiency and can effectively prevent premature convergence. Therefore, it can be employed to solve complicated machine learning problems.
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Bo, C., Zhenyu, G., Zhifeng, B., Binggang, C. (2006). Parallel Chaos Immune Evolutionary Programming. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_26
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DOI: https://doi.org/10.1007/11941439_26
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