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Genetic algorithm with a local search strategy for discovering communities in complex networks

机译:带有局部搜索策略的遗传算法用于发现复杂网络中的社区

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摘要

In order to further improve the performance of current genetic algorithms aiming at discovering communities, a local search based genetic algorithm GALS is here proposed. The core of GALS is a local search based mutation technique. In order to overcome the drawbacks of traditional mutation methods, the paper develops the concept of marginal gene and then the local monotonicity of modularity function Q is deduced from each nodes local view. Based on these two elements, a new mutation method combined with a local search strategy is presented. GALS has been evaluated on both synthetic benchmarks and several real networks, and compared with some presently competing algorithms. Experimental results show that GALS is highly effective and efficient for discovering community.
机译:为了进一步提高针对发现社区的现有遗传算法的性能,本文提出了一种基于局部搜索的遗传算法GALS。 GALS的核心是基于本地搜索的突变技术。为了克服传统突变方法的弊端,提出边缘基因的概念,然后从每个节点的局部观点推导模块化函数Q的局部单调性。基于这两个要素,提出了一种结合局部搜索策略的新变异方法。已对GALS进行了综合基准测试和一些实际网络评估,并与一些目前竞争的算法进行了比较。实验结果表明,GALS对于发现社区是非常有效和高效的。

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