Breaking the Central Bias: Spatially Partitioned Experts for Coordinate-Based Neuroevolution
Evolvable-Substrate HyperNEAT (ES-HyperNEAT), a bio-inspired indirect encoding that determines neuron placement and connection weights from spatial coordinates, exhibits a failure mode on MNIST as a diagnostic benchmark. Because input pixels map to a coordinate space centered at the origin, evolved networks converge on a small central cluster of input pixels, a spatial-concentration bias; prior work observed only 21% mean accuracy in this regime. Is this bias an optimization artifact or an architectural ceiling? Inspired by Mixture-of-Experts (MoE) principles, we partition the input into non-overlapping spatial segments, each assigned to a separately evolved specialist network. With 13 su...
| Authors | Romain Claret, Arthur Gygax, Michael O'Neill et al. |
|---|---|
| Primary category | cs.NE |
| Published | 10 September 2026 |
| Last updated at source | 10 September 2026 |
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