免疫克隆策略

Liu Ruochen, Duan Haifeng, Jiao Licheng
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引用次数: 33

摘要

本文以克隆选择理论为基础,分析了人工智能领域将要探索的克隆的主要机制。提出了一种改进的进化策略算法——免疫克隆策略算法(ICS),包括免疫单克隆策略算法(IMSA)和免疫多克隆策略算法(IPSA)。与经典的进化策略算法(CES)相比,ICS是一种能够解决多目标优化等复杂机器学习任务的进化策略,并且结果更好。利用马尔可夫链理论,证明了ICS算法的收敛性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
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