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New JAX implementation tackles ES-HyperNEAT scaling bottleneck

By PulseAugur Editorial · [1 sources] ·

Researchers have developed JAX-ESHN, a new JAX-based implementation designed to parallelize ES-HyperNEAT on GPUs. This implementation addresses the quadtree bottleneck that previously limited scalability in coordinate-based neuroevolution. Benchmarks show JAX-ESHN significantly outperforms CPU-based methods on tasks like XOR and CartPole, particularly with deep substrates, by offering lower runtime variance and more reliable success rates. The study also identifies structural constraints for scalable neuroevolution and proposes EMR-HyperNEAT as a reformulation that overcomes these limitations. AI

IMPACT This research defines structural constraints for scaling neuroevolutionary algorithms, potentially enabling more complex and efficient AI architectures.

RANK_REASON Academic paper detailing a new implementation and theoretical constraints for neuroevolution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.NE (Neural & Evolutionary) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New JAX implementation tackles ES-HyperNEAT scaling bottleneck

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Academic paper detailing a new implementation and theoretical constraints for neuroevolution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.NE (Neural & Evolutionary) TIER_1 English(EN) · Kilian Stoffel ·

    On Scaling Coordinate-Based Neuroevolution: The Quadtree Bottleneck in ES-HyperNEAT

    ES-HyperNEAT evolves substrate topology through adaptive quadtree subdivision; to our knowledge, no implementation with full population-level GPU parallelization exists. We present JAX-ESHN, a JAX-based implementation targeting GPU parallelization with batched CPPN queries, and b…

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