New arXiv Paper Introduces JAX-ESHN, a GPU-Parallel ES-HyperNEAT Implementation

A new arXiv preprint describes JAX-ESHN, a rewrite of the ES-HyperNEAT neuroevolution method using JAX to run whole populations on GPUs. The authors note that, to their knowledge, no existing implementation of the algorithm had offered this level of parallelism before. ES-HyperNEAT itself develops neural network layouts by adaptively subdividing a quadtree, and the new implementation is meant to make it faster and more practical on modern hardware.