LESS: Lightweight Evolutionary Supernet Search in Minutes

Low-cost NAS must both explore high-performing architectures and identify them reliably, yet reducing evaluation cost often weakens the fidelity of candidate comparisons. Training-free methods reduce evaluation cost by replacing learned task feedback with proxy signals measured at initialization. We introduce LESS (Lightweight Evolutionary Supernet Search), a data-driven method that combines a brief fair hard-path warm-up with discrete search under a single CMA-ES distribution. Each proposal is evaluated as its decoded hard genotype after six candidate-conditioned supernet updates. On NAS-Bench-201, LESS achieves 93.189 ± 0.467% CIFAR-10 test accuracy in 409.1 seconds, coming within 0.04 percentage points of FairNAS using approximately 1/24 of its source-reported search time. Matched controls show that calibration improves selected validation accuracy by 0.577 percentage points while changing best-visited accuracy by only 0.054 points, indicating that its primary effect is to reduce selection regret. The frozen configuration transfers without tuning to CIFAR-100 and ImageNet16-120 with 69.615 ± 1.139% and 43.720 ± 1.697% accuracy. Applied without tuning to the larger DARTS space, LESS achieves 96.95 ± 0.14% on CIFAR-10 and 82.43 ± 0.80% on CIFAR-100, with each search completing in approximately 43.5 minutes on a single GPU. Together, these results show that short, balanced, data-dependent updates enable competitive neural architecture search across datasets and search spaces within minutes.