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动态优化中并行进化算法的运行时分析

A Runtime Analysis of Parallel Evolutionary Algorithms in Dynamic Optimization.

作者信息

Lissovoi Andrei, Witt Carsten

机构信息

1Department of Computer Science, University of Sheffield, Sheffield, UK.

2DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark.

出版信息

Algorithmica. 2017;78(2):641-659. doi: 10.1007/s00453-016-0262-4. Epub 2016 Dec 7.

Abstract

A simple island model with islands and migration occurring after every iterations is studied on the dynamic fitness function Maze. This model is equivalent to a  EA if , i. e., migration occurs during every iteration. It is proved that even for an increased offspring population size up to , the  EA is still not able to track the optimum of Maze. If the migration interval is chosen carefully, the algorithm is able to track the optimum even for logarithmic . The relationship of , and the ability of the island model to track the optimum is then investigated more closely. Finally, experiments are performed to supplement the asymptotic results, and investigate the impact of the migration topology.

摘要

在动态适应度函数“迷宫”上研究了一个简单的岛屿模型,该模型有(n)个岛屿,且每(k)次迭代后发生迁移。如果(k = 1),即每次迭代都发生迁移,那么这个模型就等同于一个(n)元进化算法((n)-ary evolutionary algorithm,(n)-EA)。已证明,即使子代种群规模增加到(n^2),(n)-EA 仍然无法追踪“迷宫”的最优解。如果仔细选择迁移间隔,即使对于对数级的(n),该算法也能够追踪最优解。然后更深入地研究了(n)、(k)与岛屿模型追踪最优解能力之间的关系。最后,进行实验以补充渐近结果,并研究迁移拓扑结构的影响。

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