Rank-reliable teacher-guided fitness approximation for expensive evolutionary optimization in a TinyML neural architecture search study
Read the original at arxiv.org→arXiv:2609.30553v1 Announce Type: new Abstract: Expensive evolutionary search does not always need an exact fitness estimate for every candidate. It often needs a reliable answer to a simpler question: which...
Original headline: "Rank-Reliable Teacher-Guided Fitness Approximation for Expensive Evolutionary Optimization: A TinyML Architecture Search Study"
Coverage timeline
- Sep 28, 04:00 UTC arXiv cs.AI lead source Rank-Reliable Teacher-Guided Fitness Approximation for Expensive Evolutionary Optimization: A TinyML Architecture Search Study
- Sep 28, 04:00 UTC arXiv cs.LG ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers