GPU Computation in Bioinspired Algorithms: A Review

  • M. G. Arenas
  • A. M. Mora
  • G. Romero
  • P. A. Castillo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6691)


Bioinspired methods usually need a high amount of computational resources. For this reason, parallelization is an interesting alternative in order to decrease the execution time and to provide accurate results. In this sense, recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also refered as GPU computation. Advances in the video gaming industry have led to the production of low-cost, high-performance graphics processing units (GPUs) that possess more memory bandwidth and computational capability than central processing units (CPUs). As GPUs are available in personal computers, and they are easy to use and manage through several GPU programming languages (CUDA, OpenCL, etc.), graphics engines are being adopted widely in scientific computing applications, particularly in the fields of computational biology and bioinformatics. This paper reviews the use of GPUs to solve scientific problems, giving an overview of current software systems.


Graphic Processing Unit Quadratic Assignment Problem Island Model Parallel Genetic Algorithm Bioinspired Algorithm 
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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  1. 1.
    Thompson, C.J., Hahn, S., Oskin, M.: Using modern graphics architectures for general-purpose computing: a framework and analysis. In: Proceedings of the 35th Annual ACM/IEEE International Symposium on Microarchitecture. MICRO 35, pp. 306–317. IEEE Computer Society Press, Los Alamitos (2002)Google Scholar
  2. 2.
    Buck, I., Foley, T., Horn, D., Sugerman, J., Fatahalian, K., Houston, M., Hanrahan, P.: Brook for gpus: stream computing on graphics hardware. ACM Trans. Graph. 23, 777–786 (2004)CrossRefGoogle Scholar
  3. 3.
    Illinois, U.: The LLVM Compiler Infrastructure. University of Illinois at Urbana-Champaign (2011),
  4. 4.
    Rechenberg, I.: Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. Frommann-Hozboog, Stuttgart (1973)Google Scholar
  5. 5.
    Fogel, L.: Artificial Intelligence Through Simulated Evolution. John Wiley & Sons, Chichester (1966)zbMATHGoogle Scholar
  6. 6.
    Holland, J.H.: Adaptation in natural and artificial systems. University of Michigan, Boston (1975)Google Scholar
  7. 7.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  8. 8.
    Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (1994)zbMATHGoogle Scholar
  9. 9.
    Koza, J.R., Andre, D., Bennett III, F.H., Keane, M.: Genetic Programming 3: Darwinian Invention and Problem Solving. Morgan Kaufman, San Francisco (1999)zbMATHGoogle Scholar
  10. 10.
    Zhang, S., He, Z.: Implementation of parallel genetic algorithm based on CUDA. In: Cai, Z., Li, Z., Kang, Z., Liu, Y. (eds.) ISICA 2009. LNCS, vol. 5821, pp. 24–30. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  11. 11.
    Wong, M., Wong, T., Fok, K.: Parallel evolutionary algorithms on graphics processing unit. In: The 2005 IEEE Congress on Evolutionary Computation, vol. 3, pp. 2286–2293 (2005)Google Scholar
  12. 12.
    Harding, S., Banzhaf, W.: Fast genetic programming and artificial developmental systems on gpus. In: 21st International Symposium on High Performance Computing Systems and Applications, HPCS 2007, vol. 2 (2007)Google Scholar
  13. 13.
    Wong, M., Wong, T.: Parallel hybrid genetic algorithms on Consumer-Level graphics hardware. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 2973–2980 (2006)Google Scholar
  14. 14.
    Wong, M., Wong, T.: Implementation of parallel genetic algorithms on graphics processing units. In: et al., M.G., ed.: Intelligent and Evolutionary Systems. SCI, vol. 187, pp. 197–216. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  15. 15.
    Yu, Q., Chen, C., Pan, Z.: Parallel genetic algorithms on programmable graphics hardware. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 1051–1059. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  16. 16.
    Luo, Z., Liu, H.: Cellular genetic algorithms and local search for 3-SAT problem on graphic hardware. In: IEEE Congress on Evolutionary Computation, CEC 2006, pp. 2988–2992 (2006)Google Scholar
  17. 17.
    Selman, B., Kautz, H.: Domain-independent extensions to gsat: Solving large structured satisfiability problems. In: PROC. IJCAI 1993, vol. 93, pp. 290–295 (1993)Google Scholar
  18. 18.
    Li, J., Wang, X., He, R., Chi, Z.: An efficient fine-grained parallel genetic algorithm based on GPU-Accelerated. In: IFIP International Conference on Network and Parallel Computing Workshops, NPC 2007, pp. 855–862 (2007)Google Scholar
  19. 19.
    Li, J., Zhang, L., Liu, L.: A parallel immune algorithm based on fine-grained model with gpu-acceleration. In: Proceedings of the 2009 Fourth International Conference on Innovative Computing, Information and Control, ICICIC 2009, pp. 683–686. IEEE Computer Society, Los Alamitos (2009)CrossRefGoogle Scholar
  20. 20.
    Vidal, P., Alba, E.: Cellular genetic algorithm on graphic processing units. In: et al., J.G., ed.: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). SCI, vol. 284, pp. 223–232. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  21. 21.
    Pospichal, P., Jaros., J.: Gpu-based acceleration of the genetic algorithm. Technical report, GECOO competition (2009)Google Scholar
  22. 22.
    Tsutsui, S., Fujimoto, N.: Solving quadratic assignment problems by genetic algorithms with gpu computation: a case study. In: GECCO 2009: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, pp. 2523–2530. ACM, New York (2009)Google Scholar
  23. 23.
    Luong, T.V., Melab, N., Talbi, E.G.: GPU-based Island Model for Evolutionary Algorithms. In: Genetic and Evolutionary Computation Conference (GECCO), Portland United States (2010)Google Scholar
  24. 24.
    Pospíchal, P., Jaros, J., Schwarz, J.: Parallel genetic algorithm on the CUDA architecture. In: Di Chio, C., Cagnoni, S., Cotta, C., Ebner, M., Ekárt, A., Esparcia-Alcazar, A.I., Goh, C.-K., Merelo, J.J., Neri, F., Preuß, M., Togelius, J., Yannakakis, G.N. (eds.) EvoApplicatons 2010. LNCS, vol. 6024, pp. 442–451. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  25. 25.
    Pospíchal, P., Schwarz, J., Jaroš, J.: Parallel genetic algorithm solving 0/1 knapsack problem running on the gpu. In: 16th International Conference on Soft Computing MENDEL 2010, Brno University of Technology, pp. 64–70 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • M. G. Arenas
    • 1
  • A. M. Mora
    • 1
  • G. Romero
    • 1
  • P. A. Castillo
    • 1
  1. 1.Department of Architecture and Computer Technology. CITICUniversity of GranadaSpain

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