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Accelerating steady-state genetic algorithms based on CUDA architecture

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4 Author(s)
Masashi Oiso ; Graduate School of Engineering, Hiroshima University, Hiroshima, Japan ; Toshiyuki Yasuda ; Kazuhiro Ohkura ; Yoshiyuki Matumura

Parallel processing using graphic processing units (GPUs) have attracted much research interest in recent years. Parallel computation can be applied to genetic algorithms (GAs) in terms of the processes of individuals in a population. This paper describes the implementation of GAs in the compute unified device architecture (CUDA) environment. CUDA is a general-purpose computation environment for GPUs. The major characteristic of this study is that a steady-state GA is implemented on a GPU based on concurrent kernel execution. The proposed implementation is evaluated through four test functions; we find that the proposed implementation method is 3.0-6.0 times faster than the corresponding CPU implementation.

Published in:

2011 IEEE Congress of Evolutionary Computation (CEC)

Date of Conference:

5-8 June 2011