A Layout algorithm for data flow diagrams
IEEE Transactions on Software Engineering
Combinatorial algorithms for integrated circuit layout
Combinatorial algorithms for integrated circuit layout
Algorithms for drawing graphs: an annotated bibliography
Computational Geometry: Theory and Applications
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
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Optimal Compaction of Orthogonal Grid Drawings
Proceedings of the 7th International IPCO Conference on Integer Programming and Combinatorial Optimization
New Layout Techniques for Entity-Relationship Diagrams
Proceedings of the Fourth International Conference on Entity-Relationship Approach
Fast Compaction for Orthogonal Drawings with Vertices of Prescribed Size
GD '01 Revised Papers from the 9th International Symposium on Graph Drawing
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 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
Mathematical and Computer Modelling: An International Journal
A hybrid OC-GA approach for fast and global truss optimization with frequency constraints
Applied Soft Computing
| Hi-index | 0.00 |
This paper reflects results of research related to developing a new methodology for automatic graph drawing based on applying genetic algorithms. The methodology has permitted the elaboration of a hybrid technique that combines the most popular, classical, topology-shape-metric approach to orthogonal drawings on the grid and a genetic algorithm that is directed, in its evolutionary process, at multicriteria decision making in a fuzzy environment. In the traditional use of the topology-shape-metric approach, a single fixed planar embedding is obtained in the planarization step. Thereafter this embedding is subjected to the orthogonalization and compaction steps. However, this sequence does not guarantee that the fixed planar embedding will generate a final drawing of a good quality. Moreover, every topology-shape-metric step is classified as a NP-hard problem, and choices as well as heuristics used in previous stages have a direct impact on subsequent ones. Taking this into account, the developed hybrid technique generates a greater number of planar embeddings by varying the order of edges' insertion when forming the planar embeddings in planarization step. Thus, the problem is formulated as a permutation-based combinatorial optimization problem. The genetic algorithm is applied at the planarization step of the topology-shape-metric. This allows one to generate the population with the corresponding number of planar embeddings. Each planar embedding obtained in the planarization step is submitted to the orthogonalization and compaction. Their results serve for applying the procedures of multicriteria decision making in a fuzzy environment. Thus, in the evolutionary process, the genetic algorithm is able to select individuals, which provide more harmonious solutions (relatively of the solutions obtained with traditional applying the topology-shape-metric approach) from the point of view of the aesthetic criteria that are usually utilized at the three steps of automatic graph drawing. This is convincingly demonstrated by experimental results given in the paper.