In this research paper a hybridization of two computational intelligence fields, which are evolutionary computation techniques and complex networks (CNs), is presented. During the optimization run of the success-history based adaptive differential evolution (SHADE) a CN is built and its feature, node degree centrality, is extracted for each node. Nodes represent here the individual solutions from the SHADE population. Edges in the network mirror the knowledge transfer between individuals in SHADE’s population, and therefore, the node degree centrality can be used to measure knowledge transfer capabilities of each individual. The correlation between individual’s quality and its knowledge transfer capability is recorded and analyzed on the CEC2015 benchmark set in three different dimensionality settings—10D, 30D and 50D. Results of the analysis are discussed, and possible directions for future research are suggested.

中文翻译：

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通过复杂的网络分析SHADE中的知识转移

在这篇研究论文中，提出了两种计算智能领域的混合，即进化计算技术和复杂网络（CN）。在基于成功历史的自适应差分进化（SHADE）的优化过程中，将构建一个CN，并为每个节点提取其特征（节点度中心性）。此处的节点代表SHADE总体中的各个解决方案。网络的边缘反映了SHADE人口中个体之间的知识转移，因此，节点度中心性可用于衡量每个个体的知识转移能力。在CEC2015基准测试中，在三种不同的维度设置（10D，30D和50D）中记录并分析了个人素质与其知识传递能力之间的相关性。