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An Implementation of Evolutionary Computation Operators in OpenCL

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Intelligent Distributed Computing V

Part of the book series: Studies in Computational Intelligence ((SCI,volume 382))

Abstract

We discuss the parallel implementation of Genetic Algorithms and Evolution Strategy on General-Purpose Graphical Units, using the OpenCL framework. Multiple evolutionary operators are tested (tournament, roulette wheel selection, uniform and Gaussian mutation, crossover, recombination), as well as different approaches for parallelism, for small and large problem sizes. We use the Island Model of Parallel GA, with random migration. Performance is measured using two graphic cards: NVidia GeForce GTX 560Ti and AMD Radeon 6950. Tests are performed in a distributed grid, using the Java Parallel Processing Framework.

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References

  1. Bäck, T.: Evolutionary algorithms in theory and practice. Oxford University Press, Oxford (1996)

    MATH  Google Scholar 

  2. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Handbook of Evolutionary Computation. IOP Publishing Ltd. (1997)

    Google Scholar 

  3. Bäck, T., Fogel, D.B., Michalewicz, Z. (eds.): Basic Algorithms and Operators. IOP Publishing Ltd. (1999)

    Google Scholar 

  4. Beyer, H.G., Schwefel, H.P.: Evolution strategies A comprehensive introduction. Natural Computing 1, 3–52 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  5. Cantú-Paz, E.: A survey of parallel genetic algorithms. Calculateurs Paralleles 10 (1998)

    Google Scholar 

  6. De Jong, K.A.: An analysis of the behavior of a class of genetic adaptive systems. Ph.D. thesis, Ann Arbor, MI, USA (1975)

    Google Scholar 

  7. Harris, M.: Optimizing Parallel Reduction in CUDA. NVIDIA Developer Technology (2008)

    Google Scholar 

  8. Harris, M., Sengupta, S., Owens, J.D.: Parallel Prefix Sum (Scan) with CUDA. In: Nguyen, H. (ed.) GPU Gems 3, Addison-Wesley, Reading (2007)

    Google Scholar 

  9. Holland, J.: Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor (1975)

    Google Scholar 

  10. Khronos OpenCL Working Group: The OpenCL Specification, version 1.1 (2010), http://www.khronos.org/registry/cl/specs/opencl-1.1.pdf

  11. Lőrentz, I., Maliţa, M., Andonie, R.: Evolutionary Computation on the Connex Architecture. In: The 22nd Midwest Artificial Intelligence and Cognitive Science Conference, Cincinnati, Ohio, vol. 710, CEUR-WS.org (2011)

    Google Scholar 

  12. Marsaglia, G.: Xorshift RNGs. Journal of Statistical Software 8(14), 1–6 (2003)

    Google Scholar 

  13. Mühlenbein, H., Schomisch, M., Born, J.: The parallel genetic algorithm as function optimizer. Parallel Comput. 17, 619–632 (1991)

    Article  MATH  Google Scholar 

  14. NVIDIA: OpenCL Programming Guide for the CUDA Architecture, 3.2 edn. (2010)

    Google Scholar 

  15. Pharr, M., Fernando, R.: GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation. chap. 46. Addison-Wesley Professional, Reading (2005)

    Google Scholar 

  16. Pospichal, 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)

    Chapter  Google Scholar 

  17. Quinn, M.J.: Parallel Programming in C with MPI and OpenMP. McGraw-Hill, New York (2003)

    Google Scholar 

  18. Rechenberg, I.: Evolutionsstrategie 1994. Frommann-Holzboog (1994)

    Google Scholar 

  19. Rosenbrock, H.H.: An Automatic Method for Finding the Greatest or Least Value of a Function. The Computer Journal 3(3), 175–184 (1960)

    Article  MathSciNet  Google Scholar 

  20. JOCL - Java binding for the OpenCL API, http://jogamp.org/jocl/www/

  21. JPPF - Java Parallel Processing Framework, http://www.jppf.org/

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Author information

Authors and Affiliations

  1. Electronics and Computers Department, Transylvania University, Braşov, Romania

    István Lőrentz & Răzvan Andonie

  2. Computer Science Department, Central Washington University Ellensburg, WA, USA

    Răzvan Andonie

  3. Computer Science Department, Saint Anselm College Manchester, NH, USA

    Mihaela Maliţa

Authors
  1. István Lőrentz
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  2. Răzvan Andonie
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  3. Mihaela Maliţa
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Editor information

Editors and Affiliations

  1. Faculty of Technology,Policy andManagement, Intelligent Interactive Dynamic Systems, Section Systems Engineering, Delft University of Technology, Jaffalaan 5, 2628BX, Delft, The Netherlands

    F. M. T. Brazier

  2. D-CIS lab, P.O. Box 90, 2600 AB, Delft, The Netherlands

    Kees Nieuwenhuis  & Gregor Pavlin  & 

  3. Faculty of Technology,Policy andManagement, Section Systems Engineering, Delft University of Technology, Jaffalaan 5, 2628BX, Delft, The Netherlands

    Martijn Warnier

  4. Faculty of Automatics, Computers and Electronics, Software Engineering Department, University of Craiova, Bvd. Decebal Nr. 107, 200440, Craiova, Romania

    Costin Badica

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© 2011 Springer-Verlag Berlin Heidelberg

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Lőrentz, I., Andonie, R., Maliţa, M. (2011). An Implementation of Evolutionary Computation Operators in OpenCL. In: Brazier, F.M.T., Nieuwenhuis, K., Pavlin, G., Warnier, M., Badica, C. (eds) Intelligent Distributed Computing V. Studies in Computational Intelligence, vol 382. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24013-3_11

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  • DOI: https://doi.org/10.1007/978-3-642-24013-3_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24012-6

  • Online ISBN: 978-3-642-24013-3

  • eBook Packages: EngineeringEngineering (R0)

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