EvoTorch: Scalable Evolutionary Computation in Python

Nihat Engin Toklu, Timothy Atkinson, Vojtěch Micka, Paweł Liskowski, Rupesh Kumar Srivastava
arXiv:2302.12600 [cs.NE], (27 Feb 2023)




   author={Toklu, Nihat Engin and Atkinson, Timothy and Micka, Vojtěch and Liskowski, Paweł and Srivastava, Rupesh Kumar},

   keywords={Neural and Evolutionary Computing (cs.NE), Artificial Intelligence (cs.AI), FOS: Computer and information sciences, FOS: Computer and information sciences},

   title={EvoTorch: Scalable Evolutionary Computation in Python},



   copyright={arXiv.org perpetual, non-exclusive license}


Evolutionary computation is an important component within various fields such as artificial intelligence research, reinforcement learning, robotics, industrial automation and/or optimization, engineering design, etc. Considering the increasing computational demands and the dimensionalities of modern optimization problems, the requirement for scalable, re-usable, and practical evolutionary algorithm implementations has been growing. To address this requirement, we present EvoTorch: an evolutionary computation library designed to work with high-dimensional optimization problems, with GPU support and with high parallelization capabilities. EvoTorch is based on and seamlessly works with the PyTorch library, and therefore, allows the users to define their optimization problems using a well-known API.
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