Smartphone transillumination imaging paired with a neuroevolution-tuned ensemble model classifies chicken breast myopathies at 82.4% accuracy on 336 fillets, matching costly hyperspectral systems.
Designing neural networks through neuroevolu- tion.Nature Machine Intelligence, 1(1):24–35
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Shallow MLPs and dense CPGs outperform deeper MLPs and Actor-Critic RL in bounded robot control tasks with limited proprioception, with a Parameter Impact metric indicating extra RL parameters yield no performance gain over evolutionary strategies.
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MyoVision: A Mobile Research Tool and NEATBoost-Attention Ensemble Framework for Real Time Chicken Breast Myopathy Detection
Smartphone transillumination imaging paired with a neuroevolution-tuned ensemble model classifies chicken breast myopathies at 82.4% accuracy on 336 fillets, matching costly hyperspectral systems.
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Benefits of Low-Cost Bio-Inspiration in the Age of Overparametrization
Shallow MLPs and dense CPGs outperform deeper MLPs and Actor-Critic RL in bounded robot control tasks with limited proprioception, with a Parameter Impact metric indicating extra RL parameters yield no performance gain over evolutionary strategies.