Case Study of Evolutionary Process Visualization Using Complex Networks

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 210)

Abstract

This paper presents a case study of visualization of evolutionary process using complex network. Our previous research focused on application of evolutionary algorithms on finding global minimum of energetic function obtained in Force-directed graph drawing algorithm. This research has been combined with novel method for visualization of Differential Evolution (DE) and Self-Organizing Migration Algorithm (SOMA) process. We have developed and run our own algorithms, visualized and analyzed evolutionary complex networks obtained from their process. This paper presents improvements to the evolutionary network visualization by observing changes of some of the complex network properties during evolution. We also propose further improvements to the evolutionary network visualization.

Keywords

Force-based layout algorithm Differential evolution Soma Complex network Complex network analysis 

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References

  1. 1.
    Azad, M. A.K., Fernandes, M.G.P.: Modified constrained differential evolution for solving nonlinear global optimization problems. In: Madani, K., Correia, A.D., Rosa, A., Filipe, J. (eds.) Computational Intelligence. SCI, vol. 465, pp. 85–100. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  2. 2.
    Anand, P., Venkateswarlu, C., BhagvanthRao, M.: Multistage dynamic optimiza-tion of a copolymerization reactor using differential evolution. Asia-Pacific Journal of Chemical Engineering 14(1) (2013)Google Scholar
  3. 3.
    Sivasubramani, S., Swarup, K.S.: Multiagent–based differential evolution algorithm for loss minimisation in power systems. International Journal of Power and Energy Conversion, 34–46 (2012)Google Scholar
  4. 4.
    Lobo, F., Lima, C., Michalewicz, Z. (eds.): Parameter Setting in Evolutionary Algorithms. SCI, vol. 54. Springer (2007)Google Scholar
  5. 5.
    Nannen, V., Eiben, A.: Relevance estimation and value calibration of evolutionary algorithm parameters. In: Proceedings of the Joint International Conference for Artificial Intelligence (IJCAI), pp. 975–980 (2006)Google Scholar
  6. 6.
    Hutter, F., Hoos, H.H., Stützle, T.: Automatic algorithm configuration based on local search. In: Proceedings of the 22nd Conference on Artifical Intelligence, pp. 1152–1157 (2007)Google Scholar
  7. 7.
    Eiben, A.E., Marchiori, E., Valkó, V.A.: Evolutionary algorithms with on-the-fly population size adjustment. In: Yao, X., Burke, E.K., Lozano, J.A., Smith, J., Merelo-Guervós, J.J., Bullinaria, J.A., Rowe, J.E., Tiňo, P., Kabán, A., Schwefel, H.-P. (eds.) PPSN 2004. LNCS, vol. 3242, pp. 41–50. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. 8.
    Nannen, V., Eiben, A.E.: Relevance Estimation and Value Calibration of Evolutionary Algorithm Parameters. In: Veloso, M.M. (ed.) IJCAI, pp. 1034–1039 (2007)Google Scholar
  9. 9.
    Wang, Y.: Differential Evolution with Composite Trial Vector Generation Strategies and Control Parameters. IEEE Transactions on Evolutionary Algorithms, 55–66 (February 2011)Google Scholar
  10. 10.
    Eiben, A.E.: Parameter tuning for configuring and analyzing evolutionary algorithms. Swarm and Evolutionary Computation 1, 19–31 (2011)CrossRefGoogle Scholar
  11. 11.
    Eades, P.: A heuristic for graph drawing. Congressus Numerantium 42, 149–160 (1984)MathSciNetGoogle Scholar
  12. 12.
    Fruchterman, T., Reingold, E.: Graph drawing by force-directed placement. Softw. – Pract. Exp. 21(11), 1129–1164 (1991)CrossRefGoogle Scholar
  13. 13.
    Kamada, T., Kawai, S.: An algorithm for drawing general undirected graphs. Inform. Process. Lett. 31, 7–15 (1989)MathSciNetMATHCrossRefGoogle Scholar
  14. 14.
    Gajer, P., Goodrich, M.T., Kobourov, S.G.: A multi-dimensional ap-proach to force-directed layouts of large graphs. Computational Geometry 29, 3–18 (2004)MathSciNetMATHCrossRefGoogle Scholar
  15. 15.
    Crawford, C.: A Multilevel Force-directed Graph Drawing Algorithm Using Multi-level Global Force Approximation. In: 2012 16th International Conference on Information Visualisation, IV (2012)Google Scholar
  16. 16.
    Finkel, B., Tamassia, R.: Curvilinear graph drawing using the force-directed method. In: Pach, J. (ed.) GD 2004. LNCS, vol. 3383, pp. 448–453. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  17. 17.
    Chernobelskiy, R., Cunningham, K.I., Goodrich, M.T., Kobourov, S.G., Trott, L.: Force-Directed Lombardi-Style Graph Drawing. In: Speckmann, B. (ed.) GD 2011. LNCS, vol. 7034, pp. 320–331. Springer, Heidelberg (2011)Google Scholar
  18. 18.
    Zelinka, I.: SOMA – Self Organizing Migrating Algorithm. In: Babu, B.V., Onwubolu, G. (eds.) New Optimization Techniques in Engineering, pp. 167–218. Springer, New York (2004) ISBN 3-540-20167XCrossRefGoogle Scholar
  19. 19.
    Price, K., Storn, R.: Differential Evolution – A simple evolutionary strategy for fast optimization. In: Dr. Dobb’s Journal, 264th edn., pp. 18–24, 78 (1997)Google Scholar
  20. 20.
    Price, K.: Differential evolution: a fast and simple numerical optimizer. In: Proc. 1996 Biennial Conference of the North American Fuzzy Information Processing Society, pp. 524–527. IEEE Press, New York (1996)CrossRefGoogle Scholar
  21. 21.
    Price, K.: Genetic Annealing. Dr. Dobb’s Journal, 127–132 (October 1994)Google Scholar
  22. 22.
    Price, K.: An Introduction to Differential Evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimization, pp. 79–108. McGraw-Hill, London (1999)Google Scholar
  23. 23.
    Newman, M.E.J.: The Structure and Function of Complex networks. SIAM Rev. 45(2), 67–256 (2006)Google Scholar
  24. 24.
    Zelinka, I., Davendra, D.D., Chadli, M., Senkerik, R., Dao, T.T., Skanderova, L.: Evolutionary dynamics as the structure of complex networks. In: Zelinka, I., Snasel, V., Abraham, A. (eds.) Handbook of Optimization. ISRL, vol. 38, pp. 215–243. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  25. 25.
    Schuster, H.: Handbook of Chaos Control. Wiley-VCH (1999)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.VSB-Technical University of OstravaOstrava-PorubaCzech Republic

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