  Inspired by natural evolutionary processes, Evolutionary Computation (EC) has
established itself as a cornerstone of Artificial Intelligence. Recently, with
the surge in data-intensive applications and large-scale complex systems, the
demand for scalable EC solutions has grown significantly. However, most
existing EC infrastructures fall short of catering to the heightened demands of
large-scale problem solving. While the advent of some pioneering
GPU-accelerated EC libraries is a step forward, they also grapple with some
limitations, particularly in terms of flexibility and architectural robustness.
In response, we introduce EvoX: a computing framework tailored for automated,
distributed, and heterogeneous execution of EC algorithms. At the core of EvoX
lies a unique programming model to streamline the development of parallelizable
EC algorithms, complemented by a computation model specifically optimized for
distributed GPU acceleration. Building upon this foundation, we have crafted an
extensive library comprising a wide spectrum of 50+ EC algorithms for both
single- and multi-objective optimization. Furthermore, the library offers
comprehensive support for a diverse set of benchmark problems, ranging from
dozens of numerical test functions to hundreds of reinforcement learning tasks.
Through extensive experiments across a range of problem scenarios and hardware
configurations, EvoX demonstrates robust system and model performances. EvoX is
open-source and accessible at: https://github.com/EMI-Group/EvoX.
