  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.
