用于多峰值寻优的人工免疫克隆选择算法

The Clonal Selection Algorithm for Multimode Optimization Based on Atificial Immune Method

提出了一种执行克隆选择原则的强有力的计算方法,算法思想中考虑到在免疫响应产生的较高亲和力的抗体中,选择改进的成熟后代。这种革新算法能关于机器学习解复杂问题,实例结果表明,克隆选择算法对多峰值寻优问题有优良能力。

A powerful computational implementation of the clonal selection principle that explicitly takes into account the affinity maturation of the immune response is proposed in this paper. The algorithm is shown to be an evolutionary strategy capable of solving complex machine learning tasks, like multimode optimization.