EvoX: A Distributed GPU-accelerated Library towards Scalable Evolutionary Computation

During the past decades, evolutionary computation (EC) has demonstrated promising potential in solving various complex optimization problems of relatively small scales. Nowadays, however, ongoing developments in modern science and engineering are bringing increasingly grave challenges to the conventional EC paradigm in terms of scalability. As problem scales increase, on the one hand, the encoding spaces (i.e., dimensions of the decision vectors) are intrinsically larger; on the other hand, EC algorithms often require growing numbers of function evaluations (and probably larger population sizes as well) to work properly. To meet such emerging challenges, not only does it require delicate algorithm designs, but more importantly, a high-performance computing framework is indispensable. Hence, we develop a distributed GPU-accelerated algorithm library -- EvoX. First, we propose a generalized workflow for implementing general EC algorithms. Second, we design a scalable computing framework for running EC algorithms on distributed GPU devices. Third, we provide user-friendly interfaces to both researchers and practitioners for benchmark studies as well as extended real-world applications. Empirically, we assess the promising scalability of EvoX via a series of benchmark experiments with problem dimensions/population sizes up to millions. Moreover, we demonstrate the easy usability of EvoX by applying it to solving reinforcement learning tasks on OpenAI Gym. To the best of our knowledge, this is the first library supporting distributed GPU computing in the EC literature. The code of EvoX is available at https://github.com/EMI-Group/EvoX.