Author

Arthur Gygax

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#machine learning Preprint Sep 2026

Early-Stopping Thresholds for ES-HyperNEAT: A Data-Driven Approach from Fitness Dynamics

Most hyperparameter configurations for Evolvable-Substrate HyperNEAT (ES-HyperNEAT) produce networks that stagnate at random-guessing performance, wasting computational resources. We frame early stopping as binary classification on early fitness trajectories: for each trial, we compute the cumulative median of best-per...

Romain Claret, Arthur Gygax, Michael O'Neill et al. · 2 citations · ⚡1

#machine learning Preprint Sep 2026

Breaking the Central Bias: Spatially Partitioned Experts for Coordinate-Based Neuroevolution

Inspired by Mixture-of-Experts (MoE) principles, Evolvable-Substrate HyperNEAT is partitioned into non-overlapping spatial segments, each assigned to a separately evolved specialist network, which forces evolution to discover features across the entire image.

Romain Claret, Arthur Gygax, Michael O'Neill et al. · 2 citations