Solving Very Large Optimization Problems (Up to One Billion Variables) with a Parallel Evolutionary Algorithm in CPU and GPU

This article presents the application of a parallel evolutionary algorithm implemented in both CPU and Graphic Processing Units (GPU), to solve large instances of the noisy OneMax problem with up to one billion variables. Actually, new GPU platforms provide the computing power needed to apply massively parallel strategies to solve large problems. We report here the experimental evaluation of both CPU and GPU implementations for a compact evolutionary algorithm. the proposed method demonstrates a high problem solving efficacy and shows a good scalability behavior when facing high dimension instances of the noisy OneMax problem, improving the computational efficiency and the results reported in previous similar approaches developed on CPU.