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HiveNAS: Neural Architecture Search using Artificial Bee Colony Optimization

arXiv 2211.10250 ↗First posted 18 Nov 2022cs.NE cs.AI cs.LGcited by 0

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Abstract (arXiv)

The traditional Neural Network-development process requires substantial expert knowledge and relies heavily on intuition and trial-and-error. Neural Architecture Search (NAS) frameworks were introduced to robustly search for network topologies, as well as facilitate the automated development of Neural Networks. While some optimization approaches -- such as Genetic Algorithms -- have been extensively explored in the NAS context, other Metaheuristic Optimization algorithms have not yet been investigated. In this study, we evaluate the viability of Artificial Bee Colony optimization for Neural Architecture Search. Our proposed framework, HiveNAS, outperforms existing state-of-the-art Swarm Intelligence-based NAS frameworks in a fraction of the time.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 27 references that are in the map. First posted on arXiv 18 Nov 2022.

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References · 16

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,798
  2. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,628
  3. 2016Neural Architecture Search with Reinforcement Learningcited by 1,499
  4. 2017Learning Transferable Architectures for Scalable Image Recognitioncited by 1,101
  5. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 837
  6. 2017Progressive Neural Architecture Searchcited by 487
  7. 2016Designing Neural Network Architectures using Reinforcement Learningcited by 279
  8. 2019Random Search and Reproducibility for Neural Architecture Searchcited by 254
  9. 2018Efficient Multi-objective Neural Architecture Search via Lamarckian Evolutioncited by 153
  10. 2019Evaluating the Search Phase of Neural Architecture Searchcited by 120
  11. 2020Channel Pruning via Automatic Structure Searchcited by 85
  12. 2019CARS: Continuous Evolution for Efficient Neural Architecture Searchcited by 79
4 more
  1. 2018Path-Level Network Transformation for Efficient Architecture Searchcited by 44
  2. 2020Exploring the Loss Landscape in Neural Architecture Searchcited by 20
  3. 2019DeepSwarm: Optimising Convolutional Neural Networks using Swarm Intelligencecited by 6
  4. 2022Exploring the Intersection between Neural Architecture Search and Continual Learningcited by 4

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