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arXiv Machine Learning By Romain Claret, Michael O'Neill, Paul Cotofrei, Kilian Stoffel

Tensor-Accelerated Eager Multi-Resolution Grids for Evolving Large-Scale Substrates

Read the original on arXiv Machine Learning →

The paper introduces EMR‑HyperNEAT, an eager multi‑resolution grid approach that replaces the recursive quadtree subdivision of ES‑HyperNEAT with a parallelizable evaluation of all grid positions followed by a variance‑based filter. This reformulation removes sequential dependencies, enabling efficient batching across cores and population members, and reduces computational complexity from <O(4^D)> to <O(4^D/P)>. Experiments show 12–34× GPU speedups at depths 5–7 on XOR and higher solve rates across benchmarks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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