Dendorfer S, Kist A (2025)
Publication Type: Journal article
Publication year: 2025
Book Volume: 15
Article Number: 38996
Journal Issue: 1
DOI: 10.1038/s41598-025-25840-5
Neural Architecture Search (NAS)—combined with biology-inspired evolutionary methods—can help discover suitable architectures tailored to a given objective. A guided evolutionary approach can enhance efficiency, aiming to accelerate the discovery of top-performing architectures within a given search space. We propose a novel algorithmic framework that implements selection, crossover, and mutation operations to generate new candidate architectures during an evolutionary Neural Architecture Search: A greedy selection operator, relying solely on model accuracy data, promotes exploitation. Incorporating architecture embeddings to further refine the mutation process enhances exploration. We introduce a guided mutation approach to steer the search toward unexplored regions of the current population. The proposed strategy, PBG (Population-Based Guiding), synergizes both explorative and exploitative methods. It substantially outperforms baseline methods such as regularized evolution by being up to three times faster on NAS-Bench-101. This combined approach not only leverages the strengths of both explorative guided mutation and exploitative greedy selection strategies, but also provides a robust and efficient framework reaching competitive performance for evolutionary Neural Architecture Search across benchmarks.
APA:
Dendorfer, S., & Kist, A. (2025). Population-based guiding for evolutionary neural architecture search. Scientific Reports, 15(1). https://doi.org/10.1038/s41598-025-25840-5
MLA:
Dendorfer, Stefan, and Andreas Kist. "Population-based guiding for evolutionary neural architecture search." Scientific Reports 15.1 (2025).
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