Distributed genetic algorithms with randomized migration rate
Distributed genetic algorithms with randomized migration rate
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具有随机迁移率的分布式遗传算法
DOI：
10.1109/icsmc.1999.814175
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
M. Negami
中科院分区：
文献类型：
--
作者：
T. Hiroyasu;M. Miki;M. Negami
讨论了分布式遗传算法中迁移率随机化的影响。DGA是遗传算法的扩展算法，可以并行执行。在DGA中，基因的总种群被分成称为岛的子种群。在每个岛上，执行简单的遗传算法，在一定的迁移间隔之后，每个岛上的一些个体被移动到另一个岛上。DGA中的基因数量由迁移率决定。虽然DGA即使在目标函数有多个峰值的情况下也能找到最优解，但它们比简单的遗传算法需要更多的参数，因此更耗时。我们描述了一种新的随机迁移率的DGA(DGA/RMR)。该算法通过两种数值模拟进行了评估：Rastrigin函数和Rosenbrock函数。我们证明了这些系统存在最优参数，并用所提出的方法得到了解。这些解并不是最优解，但比使用固定迁移率的DGA得到的解要好。因此，与传统的DGA相比，DGA/RMR可能是一种耗时较少的替代方案。
Discusses the effect of randomization of migration rate in distributed genetic algorithms (DGAs). DGAs are extended algorithms of GAs that can be performed in parallel. In DGAs, the total population of genes is divided into subpopulations called islands. In each island, a simple GA is performed and some of the individuals in each island are moved to another island after a certain migration interval. The number of genes in the DGA is determined by the migration rate. Although DGAs can find optimum solutions even when there are several peaks in objective functions, they require more parameters than simple GAs and are therefore more time intensive. We describe a new DGA in which the migration rate is randomized (DGA/rmr). This algorithm is evaluated using two numerical simulations: the Rastrigin function and the Rosenbrock function. We show that optimal parameters exist in these systems and obtain solutions with the proposed approach. The solutions are not optimal, but are better than those obtained using a DGA with fixed migration rate. DGA/rmr may therefore be a less time intensive alternative to conventional DGAs.