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arXiv AI By Soumen Garai, Suman Samui

Rank-Reliable Teacher-Guided Fitness Approximation for Expensive Evolutionary Optimization: A TinyML Architecture Search Study

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The paper introduces TGL-NSGA-II, a low‑fidelity framework that uses a pretrained teacher to stratify samples by difficulty and class, then applies a short knowledge‑distillation step (KD‑Lite) before scoring candidates on a stratified evaluation set. The teacher‑guided scores are fused with a Gaussian‑process surrogate to select candidates for full evaluation, and the method is evaluated on keyword spotting and bird‑call classification tasks. Results show high Kendall‑τ values (0.74 and 0.62), a 41% reduction in proxy‑score variance, and improved hypervolume and false‑positive rates compared to full NSGA‑II, while running 2.2× faster under a constrained evaluation budget.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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