Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit

  • Tien-Tsin Wong
  • Man Leung Wong
Part of the Studies in Computational Intelligence book series (SCI, volume 22)


Evolutionary Algorithms (EAs) are effective and robust methods for solving many practical problems such as feature selection, electrical circuits synthesis, and data mining. However, they may execute for a long time for some difficult problems, because several fitness evaluations must be performed. A promising approach to overcome this limitation is to parallelize these algorithms. In this chapter, we propose to implement a parallel EA on consumer-level Graphics Processing Unit (GPU). We perform experiments to compare our parallel EA with an ordinary EA and demonstrate that the former is much more effective than the latter. Since consumer-level graphics processing units are already widely available and installed on oridinary personal computers and they are easy to use and manage, more people will be able to use our parallel algorithm to solve their problems encountered in real-world applications.


Graphic Process Unit Input Texture Average Execution Time Graphic Process Unit Implementation Graphic Process Unit Memory 
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Copyright information

© Springer 2006

Authors and Affiliations

  • Tien-Tsin Wong
    • 1
  • Man Leung Wong
    • 2
  1. 1.Department of Computer Science and EngineeringThe Chinese University of Hong KongShatinHong Kong
  2. 2.Department of ComputingDecision Sciences Lingnan UniversityTuen MunHong Kong

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