A Fast Parallel Genetic Algorithm for Graph Coloring Problem Based on CUDA

Abstract

Graph coloring problem (GCP) has proven to be an NP hard problem, until now there is no way to solve it in polynomial time. In this paper, a novel parallel genetic algorithm is presented to solve the GCP based on Compute Unified Device Architecture (CUDA). The initialization, crossover, mutation and selection operators are designed parallel in threads. Moreover, the performance of our algorithm is compared with the other graph coloring algorithms using benchmark graphs, and experimental results show that our algorithm converges much more quickly than other algorithms and achieves competitive performance for solving graph coloring problem.