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Minimizing crossings in hierarchical digraphs with a hybridized genetic algorithm. (English) Zbl 1122.68087
Summary: 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.
68R10 Graph theory (including graph drawing) in computer science
90C59 Approximation methods and heuristics in mathematical programming
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