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We proposed a complex network community discovery method based on parallel immune genetic algorithm for the problem of low efficiency, slow convergence rate and population degradation in community mining method based on genetic algorithm. The algorithm is making use of the principle of parallel immune system to ensure the diversity of population, and enhancing the searching ability by using a single path crossover operator in the initial population and crossover operation, in the initial population and crossover operation to enhance the ability to find the best use of the single path crossover operator, while using the improved character encoding and adaptive mutation operator to further reduce the search space, improve the population degradation phenomenon.Experiments show that the improved parallel immune genetic algorithm is used to find the problem of complex network community with high accuracy, and the effectiveness and efficiency. 发自小木虫IOS客户端 |
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lui0822_2009(fjtony163代发): 金币+20, 代发 2016-05-15 09:45:33
fjtony163: 翻译EPI+1, 代发 2016-05-15 09:45:39
lui0822_2009(fjtony163代发): 金币+20, 代发 2016-05-15 09:45:33
fjtony163: 翻译EPI+1, 代发 2016-05-15 09:45:39
| To solve the problems of low efficiency, slow convergence rate and population degradation in genetic algorithm-based community mining method, a complex network community discovery method based on parallel immune genetic algorithm was proposed. The algorithm ensures the diversity of population using the principle of parallel immune system, thus enhancing the ability to find the best use of the single path crossover operator in the initial population and crossover operation; furthermore, it reduces the search space using the improved character encoding and adaptive mutation operator, improving the population degradation phenomenon. Experiments show that the improved parallel immune genetic algorithm can be used to find the problems of complex network community with high accuracy, effectiveness and efficiency. |
5楼2016-04-15 14:21:33
gerryangel
木虫 (正式写手)
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2楼2016-04-13 19:31:47
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针对基于遗传算法的社区挖掘方法中存在运行效率低、收敛速度慢、种群退化等问题，提出了一种基于并行免疫遗传算法的复杂网络社区发现方法。利用并行免疫原理引入到选择操作中保证种群多样性；在初始化种群和交叉操作中利用单路交叉算子加强寻优能力，同时，采用改进的字符编码和自适应变异算子进一步有效缩小了搜索空间，改善种群退化现象。实验表明，使用该改进并行免疫遗传算法用来复杂网络社区发现问题具备较高的精度，以及算法的有效性与高效性。 发自小木虫IOS客户端 |
3楼2016-04-15 13:26:43
4楼2016-04-15 13:27:02
