Implementing Genetic Algorithms to CUDA Environment Using Data Parallelization
Graduate School of Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-hiroshima, Hiroshima, 739-8527, Japan
Technical Gazette, Vol.18 No.4, 2011
@article{oiso2011implementing,
title={IMPLEMENTING GENETIC ALGORITHMS TO CUDA ENVIRONMENT USING DATA PARALLELIZATION},
author={Oiso, M. and Matsumura, Y. and Yasuda, T. and Ohkura, K.},
journal={Technical Gazette},
volume={18},
number={4},
pages={511–517},
year={2011}
}
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.
January 11, 2012 by hgpu
