一种提高复杂网络鲁棒性的遗传算法

C. Pizzuti, Annalisa Socievole
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引用次数: 2

摘要

提出了一种基于遗传算法增强网络鲁棒性的方法。该方法优化了网络的有效图电阻,这是一种来自电路分析领域的鲁棒性度量,可以通过与网络相关的拉普拉斯矩阵的特征值的累积和来计算。专门的变分算子使该方法几乎总是能找到与穷举搜索得到的解一致的解。在合成网络和现实生活网络上的实验表明,该方法优于广泛研究的启发式策略,在考虑的网络中给出了高百分比的精确解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Genetic Algorithm for Improving Robustness of Complex Networks
A method to enhance the robustness of a network, based on Genetic Algorithms, is proposed. The approach optimizes the effective graph resistance of a network, a measure of robustness derived from the field of electric circuit analysis, that can be computed as a cumulative sum of the eigenvalues of the Laplacian matrix associated with the network. Specialized variation operators allow the method to find a solution almost always coinciding with that obtained by the exhaustive search. Experiments on synthetic and real life networks show that the approach outperforms heuristic strategies extensively investigated, by giving the exact solution in a high percentage of the considered networks.
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