PARADE: A Massively Parallel Differential Evolution Template for EASEA

  • Jarosław Arabas
  • Ogier Maitre
  • Pierre Collet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7269)

Abstract

This paper presents an efficient PARAllelization of Differential Evolution on GPU hardware written as an EASEA (EAsy Specification of Evolutionary Algorithms) template for easy reproducibility and re-use. We provide results of experiments to illustrate the relationship between population size and efficiency of the parallel version based on GPU related to the sequential version on the CPU. We also discuss how the population size influences the number of generations to obtain a certain level of result quality.

Keywords

Benchmark Function General Purpose Graphic Processing Unit Function Evaluation Process Pattern Search Optimization Evolution Template 
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

  • Jarosław Arabas
    • 1
  • Ogier Maitre
    • 2
  • Pierre Collet
    • 2
  1. 1.Institute of Electronic SystemsWarsaw University of TechnologyPoland
  2. 2.LSIITUniversity of StrasbourgFrance

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