Tensor-Accelerated Eager Multi-Resolution Grids for Evolving Large-Scale Substrates
In neuroevolution, indirect encoding generates neural network connectivity from a compact genome rather than specifying each connection. ES-HyperNEAT automatically discovers where to place hidden nodes by examining CPPN output patterns: it recursively subdivides space using a quadtree, expanding regions where CPPN outputs show high variance. This adaptive approach discovers network topology without manual substrate specification, extending the fixed-grid HyperNEAT framework built on NEAT. However, the quadtree resists tensorization. Each depth level depends on the parent's variance, forcing sequential evaluation. Different CPPNs produce different subdivision patterns, preventing batching....
| Authors | Romain Claret, Michael O'Neill, Paul Cotofrei et al. |
|---|---|
| Primary category | cs.NE |
| Published | 27 August 2026 |
| Last updated at source | 27 August 2026 |
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