On the Convergence of Immune Algorithms
On the Convergence of Immune Algorithms
复制标题
论免疫算法的收敛性
DOI：
--
复制
发表时间：
2007
期刊：
影响因子：
--
通讯作者：
P. S. Oliveto
中科院分区：
文献类型：
--
作者：
V. Cutello;Giuseppe Nicosia;Mario Romeo;P. S. Oliveto
免疫算法在包括优化在内的计算智能领域得到了广泛而成功的应用。鉴于此类算法的每个运算符都有大量变体，本文通过检查足以证明其收敛于优化问题全局最优解的条件来研究一般免疫算法的收敛性。此外，问题独立的上界，以保证解决方案被发现与一个定义的概率所需的代数推导出类似的方式进行，在文献中，遗传算法。再次的独立性的功能，以优化导致的上限，这是没有实际利益，确认一般的想法，当推导出时间界限的进化算法的问题类，以优化需要加以考虑
Immune algorithms have been used widely and successfully in many computational intelligence areas including optimization. Given the large number of variants of each operator of this class of algorithms, this paper presents a study of the convergence properties of immune algorithms in general, conducted by examining conditions which are sufficient to prove their convergence to the global optimum of an optimization problem. Furthermore problem independent upper bounds for the number of generations required to guarantee that the solution is found with a defined probability are derived in a similar manner as performed previously, in literature, for genetic algorithms. Again the independence of the function to be optimised leads to an upper bound which is not of practical interest, confirming the general idea that when deriving time bounds for evolutionary algorithms the problem class to be optimised needs to be considered