Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms
针对并行进化算法中迁移间隔难以设定的问题，提出根据适应度改进情况动态调整迁移间隔的自适应方案，并给出期望运行时间和通信开销的上界分析方法，在示例函数上验证了方案能匹敌甚至超越最优固定间隔。
The migration interval is one of the fundamental parameters governing the dynamic behaviour of island models. Yet, there is little understanding on how this parameter affects performance, and how to optimally set it given a problem in hand. We propose schemes for adapting the migration interval according to whether fitness improvements have been found. As long as no improvement is found, the migration interval is increased to minimise communication. Once the best fitness has improved, the migration interval is decreased to spread new best solutions more quickly. We provide a method for obtaining upper bounds on the expected running time and the communication effort, defined as the expected number of migrants sent. Example applications of this method to common example functions show that our adaptive schemes are able to compete with, or even outperform, the optimal fixed choice of the migration interval, with regard to running time and communication effort.