SOURCE-LINKED INTELLIGENCE
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 se
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-27T18:45:50.000Z
First collected: 2026-09-21T08:32:02.028Z. This is not the publication date.