A new arXiv preprint treats early stopping for Evolvable-Substrate HyperNEAT as a binary classification problem built on early fitness trajectories. Many ES-HyperNEAT hyperparameter settings yield networks stuck at random-guessing accuracy, so the authors aim to detect failing runs early and cut wasted computation. The work is a cross-listing in cs.LG and remains a preprint without peer review.
A new arXiv paper applies a Tree-structured Parzen Estimator method to tune the hyperparameters of ES-HyperNEAT, a neuroevolution technique for growing neural networks. The work analyzes both how effectively this optimization improves results and whether the best settings transfer across problems. The authors note that NEAT-family methods remain highly sensitive to hyperparameter choices.
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.