Software—Practice & Experience
DAG—a program that draws directed graphs
Software—Practice & Experience
Genetic algorithms for drawing directed graphs
Methodologies for intelligent systems, 5
Graphical models for discovering knowledge
Advances in knowledge discovery and data mining
Arc crossing minimization in hierarchical digraphs with tabu search
Computers and Operations Research
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Visualization and Navigation in Information Visualization: A Survey
IEEE Transactions on Visualization and Computer Graphics
GD '95 Proceedings of the Symposium on Graph Drawing
Experimental and Theoretical Results in Interactive Orthogonal Graph Drawing
GD '96 Proceedings of the Symposium on Graph Drawing
Crossing Reduction by Windows Optimization
GD '02 Revised Papers from the 10th International Symposium on Graph Drawing
Case study: Visualising cyberspace: information visualisation in the Harmony Internet browser
INFOVIS '95 Proceedings of the 1995 IEEE Symposium on Information Visualization
Graph Drawing Software
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
A hybrid genetic algorithm for automatic graph drawing based on the topology-shape-metric approach.
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A multiobjective genetic algorithm for automatic orthogonal graph drawing
Proceedings of the 13th annual conference on Genetic and evolutionary computation
A genetic algorithm based heuristic to the multi-period fixed charge distribution problem
Applied Soft Computing
A fuzzy genetic algorithm for automatic orthogonal graph drawing
Applied Soft Computing
Grid sifting: Leveling and crossing reduction
Journal of Experimental Algorithmics (JEA)
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Producing clear and intelligible layouts of hierarchical digraphs knows a renewed interest in information visualization. Recent experimental results show that metaheuristics are well-adapted methods for this problem. In this paper, we develop a new Hybridized Genetic Algorithm for arc crossing minimization. It follows the basic scheme of a GA with two major differences: problem-based crossovers adapted from ordering GAs are combined with a local search strategy based on averaging heuristics. Computational testing was performed on a set of 180 random hierarchical digraphs of standard sizes with various structures. Results show that the Hybridized Genetic Algorithm significantly outperforms Tabu Search--which is one of the best known methods for this problem- and also a multi-start descent except for highly connected graphs.