Complex Network Analysis of Evolutionary Algorithms Applied to Combinatorial Optimisation Problem

  • Donald Davendra
  • Ivan Zelinka
  • Roman Senkerik
  • Michal Pluhacek
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 303)

Abstract

This research analyses the development of a complex network in an evolutionary algorithm (EA). The main aim is to evaluate if a complex network is generated in an EA, and how the population can be evaluated when the objective is to optimise an NP-hard combinatorial optimisation problem. The population is evaluated as a complex network over a number of generations, and different attributes such as adjacency graph, minimal cut, degree centrality, closeness centrality, betweenness centrality, k-Clique, k-Club, k-Clan and community graph plots are analysed. From the results, it can be concluded that an EA population does behave like a complex network, and therefore can be analysed as such, in order to obtain information about population development.

Keywords

Evolutionary algorithm complex network flow shop scheduling 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Davendra, D.: Evolutionary algorithms and the edge of chaos. In: Zelinka, I., Celikovsky, S., Richter, H., Chen, G. (eds.) Evolutionary Algorithms and Chaotic Systems. SCI, vol. 267, pp. 145–161. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  2. 2.
    Zelinka, I., Davendra, D., Enkek, R., Jasek, R.: Do Evolutionary Algorithm Dynamics Create Complex Network Structures? Complex Systems 20, 127–140, 2, 0891-2513Google Scholar
  3. 3.
    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
  4. 4.
    Davendra, D., Onwubolu, G.: Forward backward transformation. In: Onwubolu, G., Davendra, D. (eds.) Differential Evolution. SCI, vol. 175, pp. 35–80. Springer, Heidelberg (2009)Google Scholar
  5. 5.
    Price, K., Storn, R., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optimization. Springer, Germany (2005)MATHGoogle Scholar
  6. 6.
    Pinedo, M.: Scheduling: theory, algorithms and systems. Prentice Hall, Inc., New Jersey (1995)MATHGoogle Scholar
  7. 7.
    Taillard, E.: Benchmarks for basic scheduling problems. European Journal of Operations Research 64, 278–285 (1993)CrossRefMATHGoogle Scholar
  8. 8.
    Wolfram Website, http://www.wolfram.com/

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Donald Davendra
    • 1
  • Ivan Zelinka
    • 1
  • Roman Senkerik
    • 2
  • Michal Pluhacek
    • 2
  1. 1.Faculty of Electrical Engineering and Computer ScienceVŠB - Technical University of OstravaOstrava-PorubaCzech Republic
  2. 2.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

Personalised recommendations