Accelerating Steady-State Genetic Algorithms based on CUDA Architecture

被引：0

作者：

Oiso, Masashi ^{[1
]}

Yasuda, Toshiyuki ^{[1
]}

Ohkura, Kazuhiro ^{[1
]}

Matumura, Yoshiyuki ^{[2
]}

机构：

[1] Hiroshima Univ, Grad Sch Engn, Hiroshima, Japan

[2] Shinshu Univ, Fac Text, Ueda, Japan

来源：

2011 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC) | 2011年

关键词：

GP;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：687 / 692

页数：6

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