EvoJAX: Hardware-Accelerated Neuroevolution

We present EvoJAX, a scalable, general purpose, hardware-accelerated neuroevolution toolkit.

Building on top of the JAX library, our toolkit enables neuroevolution algorithms to work with neural networks running in parallel across multiple TPU/GPUs.

EvoJAX achieves very high performance by implementing the evolution algorithm, neural network and task all in NumPy, which is compiled just-in-time to run on accelerators.

We provide extensible examples of EvoJAX for a wide range of tasks, including supervised learning, reinforcement learning and generative art.

Since EvoJAX can find solutions to most of these tasks within minutes on a single accelerator, compared to hours or days when using CPUs, we believe our toolkit can significantly shorten the iteration time of conducting experiments for researchers working with evolutionary computation.


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