Abstract
Computation methods using Graphic Processing Units (GPU) for solving parallelizable problems have attracted many research interest in recent years. Following up this trend, implementations of Genetic Algorithms (GA) to GPU have been reported based on parallelism in computation tasks of population in several researches. This paper proposes an implementation method of GA on the CUDA environment, which is a general purpose computation environment for GPUs provided by NVIDIA, adopting not only parallelization of population but that of individuals. The performance of proposed implementation method are compared to a CPU implementation by the computation time using test functions and an Evolutionary Robotics problem. The proposed implementation method generated 7.6-23.0 times faster results than those of a CPU implementation.
