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Impact of the Topology on the Performance of Distributed Differential Evolution

  • Ivanoe De Falco
  • Antonio Della Cioppa
  • Domenico Maisto
  • Umberto Scafuri
  • Ernesto Tarantino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8602)

Abstract

Migration topology plays a key role in designing effective distributed evolutionary algorithms. In this work we investigate the impact of several network topologies on the performance of a stepping–stone structured Differential Evolution model. Although some issues on the control parameters of the migration process and the way they affect the efficiency of the algorithm and the solution quality deserve further evaluative study, the influence of the topology on the performance both in terms of solution quality and convergence rate emerges from the empirical findings carried out on a set of test problems.

Keywords

Network Topology Differential Evolution Island Model Parallel Genetic Algorithm Connectivity Degree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Holland, J.: Adaptation in natural and artificial systems. The University of Michigan Press, Ann Arbor (1975)Google Scholar
  2. 2.
    Schwefel, H.: Numerical optimization of computer models. Wiley & Sons (1981)Google Scholar
  3. 3.
    Goldberg, D.: Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Professional (1989)Google Scholar
  4. 4.
    Koza, J.: Genetic programming. MIT Press, Cambridge (1992)MATHGoogle Scholar
  5. 5.
    Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. Oxford University Press, Oxford (1997)MATHGoogle Scholar
  6. 6.
    De Falco, I., Cioppa, D.A., Iazzetta, A., Tarantino, E.: An evolutionary approach for automatically extracting intelligible classification rules. Knowledge and Information Systems 7, 179–201 (2005)CrossRefGoogle Scholar
  7. 7.
    Cantú-Paz, E.: A summary of research on parallel genetic algorithms. Technical Report 95007, University of Illinois, Urbana-Champaign, USA (1995)Google Scholar
  8. 8.
    Mühlenbein, H.. In: Rawlins, G. (ed.) Foundations of Genetic Algorithms. Morgan Kaufmann, San Mateo (1991)Google Scholar
  9. 9.
    Tomassini, M.: Spatially structured evolutionary algorithms. Springer (2005)Google Scholar
  10. 10.
    Cantú-Paz, E.: Efficient and accurate parallel genetic algorithms, vol. 1. Kluwer Academic Publisher, Norwell (2000)MATHGoogle Scholar
  11. 11.
    Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. on Evolutionary Computation 6, 443–462 (2002)CrossRefGoogle Scholar
  12. 12.
    Zaharie, D., Petcu, D.: Parallel implementation of multipopulation differential evolution. In: Proceedings of the Nato Advanced Research Workshop on Concurrent Information Processing and Computing, pp. 223–232. IOS Press (2003)Google Scholar
  13. 13.
    Tasoulis, D., Pavlidis, N., Plagianakos, V., Vrahatis, M.: Parallel differential evolution. Proceedings of the Congress on Evolutionary Computation. 2, 2023–2029 (2004)Google Scholar
  14. 14.
    De Falco, I., Della Cioppa, A., Scafuri, U., Tarantino, E.: A distributed differential evolution approach for mapping in a grid environment. In: Proceedings of the Fifteenth EUROMICRO International Conference on Parallel, Distributed and Network-Based Processing, pp. 442–449. IEEE Press (2007)Google Scholar
  15. 15.
    Apolloni, J., Leguizamón, G., García-Nieto, J., Alba, E.: Island based distributed differential evolution: an experimental study on hybrid testbeds. In: Proceedings of the Eight International Conference on Hybrid Intelligent Systems, pp. 696–701. IEEE Press (2008)Google Scholar
  16. 16.
    Weber, M., Neri, F., Tirronen, V.: Distributed differential evolution with explorative-exploitative population families. Genetic Programming and Evolvable Machines 10, 343–371 (2009)CrossRefGoogle Scholar
  17. 17.
    Ishimizu, T., Tagawa, K.: A structured differential evolution for various network topologies. International Journal of Computers and Communications 4, 2–8 (2010)Google Scholar
  18. 18.
    Weber, M., Neri, F., Tirronen, V.: A study on scale factor in distributed differential evolution. Information Sciences 18, 2488–2511 (2011)CrossRefGoogle Scholar
  19. 19.
    Price, K., Storn, R.: Differential evolution. Dr. Dobb’s Journal 22, 18–24 (1997)Google Scholar
  20. 20.
    Price, K., Storn, R.M., Lampinen, J.: Differential Evolution - A Practical Approach to Global Optmization. Springer (2005)Google Scholar
  21. 21.
    Nobakhti, A., Wang, H.: A simple self-adaptive differential evolution algorithm with application on the alstom gasifier. Applied Soft Computing 8, 350–370 (2008)CrossRefGoogle Scholar
  22. 22.
    De Falco, I., Della Cioppa, A., Maisto, D., Scafuri, U., Tarantino, E.: Satellite Image Registration by Distributed Differential Evolution. In: Giacobini, M. (ed.) EvoWorkshops 2007. LNCS, vol. 4448, pp. 251–260. Springer, Heidelberg (2007)Google Scholar
  23. 23.
    Alba, E., Troya, J.: A survey of parallel distributed genetic algorithms. Complexity 4, 31–52 (1999)CrossRefMathSciNetGoogle Scholar
  24. 24.
    Alba, E., Luque, G.: Theoretical models of selection pressure for dEAs: topology influence. In: Proceedings of the IEEE International Conference on Evolutionary Computation, pp. 214–221 (2005)Google Scholar
  25. 25.
    De Falco, I., Della Cioppa, A.: Biological invasion-inspired migration in distributed evolutionary algorithms. Information Sciences 207, 50–65 (2012)CrossRefGoogle Scholar
  26. 26.
    Skolicki, K., De Jong, K.: The influence of migration sizes and intervals on island models. In: Proceedings of the Conference of Genetic and Evolutionary Computation, Association for Computing Machinery Inc, pp. 1295–1302. ACM (2005)Google Scholar
  27. 27.
    Lässig, J., Sudholt, D.: Design and analysis of migration in parallel evolutionary algorithms. Soft Computing 17, 1121–1144 (2013)CrossRefGoogle Scholar
  28. 28.
    Suganthan, P., Hansen, N., Liang, J., Deb, K., Chen, Y., Auger, A., Tiwari, S.: Problem definitions and evaluation criteria for the CEC 2005 special session on real-parameter optimization. Technical Report 201212, Zhengzhou University, China and Nanyang Technological University, Singapore (2005)Google Scholar
  29. 29.
    Rönkkönen, J., Kukkonen, S., Price, K.: Real-parameter optimization with differential evolution. In: Proceedings of the IEEE Congress on Evolutionary Computation, vol. 1, pp. 506–513. IEEE (2005)Google Scholar
  30. 30.
    Derrac, J., García, S., Molina, D., Herrera, F.: A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms. Swarm and Evolutionary Computation 1, 3–18 (2011)CrossRefGoogle Scholar
  31. 31.
    García, S., Fernández, A., Luengo, J., Herrera, F.: Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180, 2044–2064 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Ivanoe De Falco
    • 1
  • Antonio Della Cioppa
    • 2
  • Domenico Maisto
    • 1
  • Umberto Scafuri
    • 1
  • Ernesto Tarantino
    • 1
  1. 1.ICAR-CNRNaplesItaly
  2. 2.Natural Computation Lab, DIEMUniversity of SalernoFiscianoItaly

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