Abstract
An Island GA is one of the parallel models of GAs. In an Island GA, each sub-population evolves and is processed in parallel. Therefore, an Island GA is suitable for parallel computing. Now then, GPU (Graphics Processing Units) which has better performance than CPU is attracting large attention in the parallel computing domain. Consequently, GPU is expected to be applied not only for graphics processing but also for general computation applications, and this technology is called GPU computing. This paper proposes an effective implementation method of an Island GA with CUDA based on reduction of the on-chip memory usage in order to apply to real-world problems.
