Implementation of Parallel Genetic Algorithms on Graphics Processing Units

  • Man Leung Wong
  • Tien Tsin Wong
Part of the Studies in Computational Intelligence book series (SCI, volume 187)


In this paper, we propose to parallelize a Hybrid Genetic Algorithm (HGA) on Graphics Processing Units (GPUs) which are available and installed on ubiquitous personal computers. HGA extends the classical genetic algorithm by incorporating the Cauchy mutation operator from evolutionary programming. In our parallel HGA, all steps except the random number generation procedure are performed in GPU and thus our parallel HGA can be executed effectively and efficiently. We suggest and develop the novel pseudo-deterministic selection method which is comparable to the traditional global selection approach with significant execution time performance advantages.We perform experiments to compare our parallel HGA with our previous parallel FEP (Fast Evolutionary programming) and demonstrate that the former is much more effective and efficient than the latter. The parallel and sequential implementations of HGA are compared in a number of experiments, it is observed that the former outperforms the latter significantly. The effectiveness and efficiency of the pseudo-deterministic selection method is also studied.


Graphic Processing Unit Hybrid Genetic Algorithm Graphic Hardware Average Execution Time Parallel Genetic 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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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Man Leung Wong
    • 1
  • Tien Tsin Wong
    • 2
  1. 1.Department of Computing and Decision SciencesLingnan University, Tuen MunHong Kong
  2. 2.Department of Computer Science and EngineeringThe Chinese University of Hong Kong, ShatinHong Kong

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