Genetic algorithms for drawing directed graphs
Methodologies for intelligent systems, 5
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
Recombination Operators for Evolutionary Graph Drawing
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Which Aesthetic has the Greatest Effect on Human Understanding?
GD '97 Proceedings of the 5th International Symposium on Graph Drawing
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These past few years genetic algorithms and stochastic hill-climbing have received a growing interest for different graph drawing problems. This paper deals with the layered drawing of directed graphs which is known to be an NP-complete problem for the arc-crossing minimization criterium. Before setting out a (n+1)th comparison between meta-heuristics, we here prefer to study the characteristics of the arccrossings landscape for three local transformations (greedy switching, barycenter, median) adapted from the Sugiyama heuristic and we propose a descriptive analysis of the landscape for two graph families. First, all the possible layouts of 2021 small graphs are generated and the optima (number, type, height, attracting sets) are precisely defined. Then, a second family of 305 larger graphs (up to 90 vertices) is examined with one thousand hill-climbers. This study highlights the diversity of the encountered configurations and gives leads for the choice of efficient heuristics.