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Two Ports of a Full Evolutionary Algorithm onto GPGPU

  • Ogier Maitre
  • Nicolas Lachiche
  • Pierre Collet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7401)

Abstract

This paper presents two parallelizations of a standard evolutionary algorithm on an NVIDIA GPGPU card, thanks to a parallel replacement operator.

These algorithms tackle new problems where previously presented approaches do not obtain satisfactory speedup. If programming is more complicated and fewer options are allowed, the whole algorithm is executed in parallel, thereby fully exploiting the intrinsic parallelism of EAs and the many available GPGPU cores.

Finally, the method is validated using two benchmarks.

Keywords

Evolutionary Algorithm Shared Memory Global Memory Single Instruction Multiple Data Evaluation Step 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ogier Maitre
    • 1
  • Nicolas Lachiche
    • 1
  • Pierre Collet
    • 1
  1. 1.LSIITUniversity of StrasbourgIllkirchFrance

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