Visualization of Large-Scale Weighted Clustered Graph: A Genetic Approach

Jiayu Zhou, Youfang Lin, Xi Wang

In this paper, a bottom-up hierarchical genetic algorithm is proposed to visualize clustered data into a planar graph. To achieve global optimization by accelerating local optimization process, we introduce sub-graph rotating and scaling processes into the genetic algorithm. Compared with existing methods, the proposed approach is more feasible and promising, with more accurate graph layout and more satisfiable computationally efficient performance, as demonstrated by the experimental results.

Subjects: 1.9 Genetic Algorithms; 11. Knowledge Representation

Submitted: Apr 7, 2008

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