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early-stopping

topic1 events
papersTODAY 04:00 UTC

Paper Proposes Data-Driven Early Stopping for ES-HyperNEAT

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.

neuroevolution/hyperneat/research/training-efficiency