EvoX: A Distributed GPU-Accelerated Framework for Scalable Evolutionary Computation

• Published in: IEEE transactions on evolutionary computation Vol. 29; no. 5; pp. 1649 - 1662
• Main Authors: Huang, Beichen, Cheng, Ran, Li, Zhuozhao, Jin, Yaochu, Tan, Kay Chen
• Format: Journal Article
• Language: English
• Published: IEEE 01.10.2025
• Subjects: Computational modeling; Distributed computing; Evolutionary computation; evolutionary reinforcement learning; GPU acceleration; Libraries; neuroevolution; Python; scalable evolutionary computation (EC); Sociology; Statistics; Task analysis
• ISSN: 1089-778X; 1941-0026
• DOI: 10.1109/TEVC.2024.3388550

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 .