An Efficient Fine-grained Parallel Genetic Algorithm Based on GPU-Accelerated

Abstract

Fine-grained parallel genetic algorithm (FGPGA), though a popular and robust strategy for solving complicated optimization problems, is sometimes inconvenient to use as its population size is restricted by heavy data communication and the parallel computers are relatively difficult to use, manage, maintain and may not be accessible to most researchers. In this paper, we propose a FGPGA method based on GPU-acceleration, which maps parallel GA algorithm to texture-rendering on consumer-level graphics cards. The analytical results demonstrate that the proposed method increases the population size, speeds up its execution and provides ordinary users with a feasible FGPGA solution.