Empirical investigations on parallel competent genetic algorithms
Empirical investigations on parallel competent genetic algorithms
复制标题
DOI：
10.1145/1389095.1389292
复制
发表时间：
2008-07
期刊：
影响因子：
--
通讯作者：
Miwako Tsuji;M. Munetomo;K. Akama
中科院分区：
文献类型：
--
作者：
Miwako Tsuji;M. Munetomo;K. Akama
本文对并行胜任遗传算法进行了实证研究[4]。循环遗传算法，如BOA[21]、LINCGA[15]、D5-GA[28]，可以通过自动学习问题的结构作为基因连锁来解决遗传算法的难点问题。通过链接学习，cGAS的并行实现可以降低计算成本，并为我们提供解决广泛现实问题的环境。虽然已经提出了一些并行的cGA[16，18，19]，但这些并行化的效果还没有得到足够的研究。本文对并行遗传算法的适用性和性能进行了实证分析，其中包括一种新的并行遗传算法--并行D5-GA。
This paper empirically investigates parallel competent genetic algorithms (cGAs) [4]. cGAs, such as BOA [21], LINCGA [15], D5-GA [28], can solve GA-difficult problems by automatically learning problem structure as gene linkage. Parallel implementation of cGAs can reduce computational cost due to the linkage learning and give us problem solving environments for a wide spectrum of real-world problems. Although some parallel cGAs have been proposed [16, 18, 19], the effect of the parallelizations has not been investigated enough. This paper empirically discusses the applicability and property of parallel cGAs, including a new parallel cGA, parallel D5-GA.