A Layout algorithm for data flow diagrams
IEEE Transactions on Software Engineering
Algorithms for drawing graphs: an annotated bibliography
Computational Geometry: Theory and Applications
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Spirality and Optimal Orthogonal Drawings
SIAM Journal on Computing
On the complexity of orthogonal compaction
Computational Geometry: Theory and Applications
Graph Drawing: Algorithms for the Visualization of Graphs
Graph Drawing: Algorithms for the Visualization of Graphs
Optimal Compaction of Orthogonal Grid Drawings
Proceedings of the 7th International IPCO Conference on Integer Programming and Combinatorial Optimization
Fast Compaction for Orthogonal Drawings with Vertices of Prescribed Size
GD '01 Revised Papers from the 9th International Symposium on Graph Drawing
Graph Layout Using a Genetic Algorithm
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Minimizing crossings in hierarchical digraphs with a hybridized genetic algorithm
Journal of Heuristics
Evolutionary layout of UML class diagrams
SoftVis '06 Proceedings of the 2006 ACM symposium on Software visualization
Hybrid multiobjective optimization genetic algorithms for graph drawing
Proceedings of the 9th 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 fuzzy genetic algorithm for automatic orthogonal graph drawing
Applied Soft Computing
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This paper presents a new approach for automatic graph drawing based on Genetic algorithms. The classical topology-shape-metric approach for orthogonal graph drawing keeps a fixed planar embedding obtained in its first step (planarization), using it for the next two steps (orthogonalization and compaction). However, each step is itself an NP-hard problem, and the choices made and heuristics used on previous stages have a direct impact on subsequent ones. We can, alternatively, obtain a large number of planar embeddings by varying the order of insertion of the graph's edges when constructing such embeddings. Following that, the genetic algorithm is applied to select the planar embeddings that would lead to the final best drawing, after evaluating its performance on the subsequent orthogonalization and compaction steps. We formulate the problem of finding an optimal planar embedding for the graph as a permutation-based combinatorial optimization problem. The problem is then solved with the genetic algorithm, using appropriate selection, crossover and mutation operators, which were adapted from other permutation-based optimization problems, such as scheduling problems. The results show that our approach is able to find better solutions, representing improved final graph drawings than the ones found via the classical topology-shape-metric approach.