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The island model for evolutionary algorithms allows to delay the global convergence of the evolution process and encourage diversity. However, solving large size and time-intensive combinatorial optimization problems with the island model requires a large amount of computational resources. GPU computing is recently revealed as a powerful way to harness these resources. In this paper, we focus on the parallel island model on GPU. We address its re-design, implementation, and associated issues related to the GPU execution context. The preliminary results demonstrate the effectiveness of the proposed approaches and their capabilities to fully exploit the GPU architecture.