Concurrent Differential Evolution Based on Generational Model for Multi-core CPUs

  • Kiyoharu Tagawa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7673)


In order to utilize multi-core CPUs more effectively, a new Concurrent Differential Evolution (CDE) is proposed. Then the proposed CDE (CDE/G) is compared with a conventional CDE (CDE/S). CDE/S uses only one population because it is based on the steady-state model. Therefore, CDE/S requires a time-consuming mutual exclusion or “lock” for every read-write access to the population. On the other hand, CDE/G is based on the generational model. By using a secondary population in addition to a primary one, CDE/G does not require any lock on the population and therefore is faster. Through the numerical experiment and the statistical test, it is demonstrated that CDE/G is superior to CDE/S in not only the run-time but also the quality of solutions.


concurrent program parallel processing multi-core CPU evolutionary algorithm differential evolution statistical test 


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  1. 1.
    Storn, R.M., Price, K.V.: Differential evolution - a simple and efficient heuristic for global optimization over continuous space. Journal of Global Optimization 11(4), 341–359 (1997)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    Das, S., Suganthan, P.N.: Differential evolution - a survey of the state-of-the art. IEEE Trans. on Evolutionary Computation 15(1), 4–31 (2011)CrossRefGoogle Scholar
  3. 3.
    Cantú-Paz, E.: Efficient and Accurate Parallel Genetic Algorithms. Kluwer Academic Publishers (2001)Google Scholar
  4. 4.
    Alba, E., Tomassini, M.: Parallelism and evolutionary algorithms. IEEE Trans. on Evolutionary Computation 5(6), 443–462 (2002)CrossRefGoogle Scholar
  5. 5.
    Tasoulis, D.K., Pavlidis, N.G., Plagianakos, V.P., Vrahatis, M.N.: Parallel differential evolution. In: Proc. of IEEE Congress on Evolutionary Computation, pp. 2023–2029 (2004)Google Scholar
  6. 6.
    Zhou, C.: Fast parallelization of differential evolution algorithm using MapReduce. In: Proc. of Genetic and Evolutionary Computation Conference, pp. 1113–1114 (2010)Google Scholar
  7. 7.
    Dorronsoro, B., Bouvry, P.: Improving classical and decentralized differential evolution with new mutation operator and population topologies. IEEE Trans. on Evolutionary Computation 1(15), 67–98 (2011)CrossRefGoogle Scholar
  8. 8.
    de Veronses, L., Krohling, R.: Differential evolution algorithm on the GPU with C-CUDA. In: Proc. of IEEE Congress on Evolutionary Computation, pp. 1–7 (2010)Google Scholar
  9. 9.
    Breshears, C.: The Art of Concurrency - A Thread Monkey’s Guide to Writing Parallel Applications. O’Reilly (2009)Google Scholar
  10. 10.
    Tagawa, K., Ishimizu, T.: Concurrent differential evolution based on MapReduce. International Journal of Computers 4(4), 161–168 (2010)Google Scholar
  11. 11.
    Syswerda, G.: A study of reproduction in generational and steady-state genetic algorithms. In: Foundations of Genetic Algorithms 2, pp. 94–101. Morgan Kaufmann Publisher (1991)Google Scholar
  12. 12.
    Feoktistov, V.: Differential Evolution in Search Solutions. Springer (2006)Google Scholar
  13. 13.
    Tagawa, K., Ishimizu, T.: A comparative study of distance dependent survival selection for sequential DE. In: Proc. of IEEE International Conference on System, Man, and Cybernetics, pp. 3493–3500 (2010)Google Scholar
  14. 14.
    Davison, B.D., Rasheed, K.: Effect of global parallelism on a steady state GA. In: Proc. of Genetic and Evolutionary Computation Conference Workshops, Evolutionary Computation and Parallel Processing Workshop, pp. 167–170 (1999)Google Scholar
  15. 15.
    Tagawa, K.: A statistical study of concurrent differential evolution on multi-core CPUs. In: Proc. of Italian Workshop on Artificial Life and Evolutionary Computation, pp. 1–12 (2012)Google Scholar
  16. 16.
    Göetz, B., Peierls, T., Bloch, J., Bowbeer, J., Holmes, D., Lea, D.: Java Concurrency in Practice. Addison-Wesley (2006)Google Scholar
  17. 17.
    Sheskin, D.J.: Handbook of Parametric and Nonparametric Statistical Procedures, 5th edn. CRC Press (2011)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kiyoharu Tagawa
    • 1
  1. 1.School of Science and EngineeringKinki UniversityHigashiJapan

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