Original scientific paper

Implementing genetic algorithms to CUDA environment using data parallelization

Masashi Oiso ; Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8527, Japan
Yoshiyuki Matsumura ; Faculty of Textile, Shinshu University, 3-15-1 Tokida, Ueda, Nagano, 386-0018, Japan
Toshiyuki Yasuda ; Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8527, Japan
Kazuhiro Ohkura ; Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8527, Japan

Abstract

Computation methods of parallel problem solving using graphic processing units (GPUs) have attracted much research interests in recent years. Parallel computation can be applied to genetic algorithms (GAs) in terms of the evaluation process of individuals in a population. This paper describes yet another implementation method of GAs to the CUDA environment where CUDA is a general-purpose computation environment for GPUs provided by NVIDIA. The major characteristic point of this study is that the parallel processing is adopted not only for individuals but also for the genes in an individual. The proposed implementation is evaluated through eight test functions. We found that the proposed implementation method yields 7,6-18,4 times faster results than those of a CPU implementation.

Keywords

Genetic Algorithms; GPU; CUDA

Hrčak ID:

75397

URI

https://hrcak.srce.hr/75397

Publication date:

27.12.2011.

Article data in other languages: croatian

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