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A Contribution to the Study of the Fitness Landscape for a Graph Drawing Problem

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Applications of Evolutionary Computing (EvoWorkshops 2001)

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Abstract

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 arc-crossings 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.

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References

  1. G. Di-Battista, P. Eades, R. Tamassia, and I.-G. Tollis. Graph drawing-Algorithms for the visualization of graphs. Prentice-Hall, 1999.

    Google Scholar 

  2. L.-J. Groves, Z. Michalewicz, P.-V. Elia, and C.-Z. Janikow. Genetic algorithms for drawing directed graphs. In Proc. of the 5 th Int. Symp. on Methodologies for Intelligent Systems, pages 268–276. Elsevier, 1990.

    Google Scholar 

  3. T. Masui. Graphic object layout with interactive genetic algorithms. In Proc. of the 1992 IEEE Workshop on Visual Languages, pages 74–80. IEEE Comp. Soc. Press, 1992.

    Google Scholar 

  4. E. Makinen and M. Sieranta. Genetic algorithms for drawing bipartite graphs. Int. J. of Computer Mathematics, 53(3-4):157–166, 1994.

    Article  MATH  Google Scholar 

  5. A. Ochoa-Rodrìguez and A. Rosete-Suàrez. Automatic graph drawing by genetic search. In Proc. of the 11 th> Int. Conf. on CAD, CAM, Robotics and Manufactories of the Future, pages 982–987, 1995.

    Google Scholar 

  6. J. Utech, J. Branke, H. Schmeck, and P. Eades. An evolutionary algorithm for drawing directed graphs. In Proc. of the Int. Conf. on Imaging Science, Systems and Technology, pages 154–160. CSREA Press, 1998.

    Google Scholar 

  7. C. Kosak, J. Marks, and S. Shieber. A parallel genetic algorithm for network-diagram layout. In Proc. of the 4 th Int. Conf. on Genetic Algorithms, pages 458–465. Morgan-Kaufmann, 1991.

    Google Scholar 

  8. C. Kosak, J. Marks, and S. Shieber. Automating the layout of network diagrams with specified visual organization. In IEEE Transactions on System, Man and Cybernetics, volume 24, pages 440–454, 1994.

    Article  Google Scholar 

  9. A. Rosete and A. Ochoa. Genetic graph drawing. In Proc. of the 13th Int. Conf. on Appl. of Artificial Intelligence in Engineering, 1998.

    Google Scholar 

  10. D. Kobler and A. Tettamanzi. Recombination operators for evolutionary graph drawing. In Proc. of Parallel Problem Solving from Nature PPSN-V, volume 1498 of Lect. Notes in Comp. Sc., pages 988–997. Springer-Verlag, 1998.

    Google Scholar 

  11. A. Rosete-Suarez, M. Sebag, and A. Ochoa-Rodriguez. A study of evolutionary graph drawing. Rapport de Recherche 1228, LRI, UMR 8623, Bat. 490, Université Paris-Sud XI, 91405 Orsay-CEDEX, France, Septembre 1999. http://www.lri.fr/~rosette-type.html.

  12. A. Rosete-Suàrez, A. Ochoa-Rodrìguez, and M. Sebag. Automatic graph drawing and stochastic hill climbing. In Proc. of the Genetic and Evolutionary Conf., GECCO’99, volume 2, pages 1699–1706. Morgan Kaufmann, 1999.

    Google Scholar 

  13. P. Kuntz, R. Lehn, and H. Briand. Dynamic rule graph drawing by genetic search. In Proc. of the IEEE Int. Conf. on System Man and Cybernetics, 2000.

    Google Scholar 

  14. S. Baluja. An empirical comparison of seven iterative and evolutionary heuristics for static functions of optimization. In Proc. of the 11th Int. Conf. on System Engineering, pages 692–697. Univ. of Nevada, 1996.

    Google Scholar 

  15. A. Juels and M. Wattenberg. Hillclimbing as a baseline method for the evaluation of stochastic optimization algorithms. In D.S. Touretsky and al, editors, Advances in Neural Information Processing Systems, pages 430–436. MIT Press, 1995.

    Google Scholar 

  16. O.J. Sharpe. Towards a rational methodology for using evolutionary search algorithms. PhD thesis, Univ. of Sussex, Brighton, UK, 2000.

    Google Scholar 

  17. M. Mitchell, J. Holland, and S. Forrest. When will a genetic algorithm outperform hill climbing? In J. Cowan, G. Tesauro, and J. Alspector, editors, Advances in Neural Information Processing Systems. Morgan Kauffman, 1994.

    Google Scholar 

  18. P. Eades and N. Wormald. Edge crossings in drawings of bipartite graphs. Algorithmica, 11:379–403, 1994.

    Article  MathSciNet  MATH  Google Scholar 

  19. K. Sugiyama, S. Tagawa, and M. Toda. Methods for visual understanding of hierarchical systems. IEEE Trans. Sys. Man and Cybernetics, 11(2):109–125, 1981.

    Article  MathSciNet  Google Scholar 

  20. V.K. Vassilev. Fitness Landscapes and Search in the Evolutionary Design of Digital Circuits. PhD thesis, Napier Univ., UK, 2000.

    Google Scholar 

  21. T.-C. Jones. Evolutionary Algorithms, Fitness Landscapes and Search. PhD thesis, University of New Mexico, Alburquerque, 1995.

    Google Scholar 

  22. T. Jones and S. Forrest. Genetic algorithms and heuristic search. In Santa Fe Institute Tech. Report 95-02-021. Santa Fe Institute, 1995.

    Google Scholar 

  23. E. Taillard. Comparison of iterative searches for the quadratic assignment problem. Location Science, 3:87–105, 1995.

    Article  MATH  Google Scholar 

  24. Purchase H. Which aesthetic has the greatest effect on human understanding ? In Proc. Graph Drawing’97, volume 1353 of Lect. Notes in Comp. Sc., pages 248–261. Springer Verlag, 1997.

    Google Scholar 

  25. D.-E. Goldberg. Genetic algorithms in search, optimization and machine learning. Addison-Wesley, 1989.

    Google Scholar 

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Lehn, R., Kuntz, P. (2001). A Contribution to the Study of the Fitness Landscape for a Graph Drawing Problem. In: Boers, E.J.W. (eds) Applications of Evolutionary Computing. EvoWorkshops 2001. Lecture Notes in Computer Science, vol 2037. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45365-2_18

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  • DOI: https://doi.org/10.1007/3-540-45365-2_18

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