A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population Mechanism Article Swipe
Yu Xue
,
Chenchen Zhu
,
MengChu Zhou
,
Mohamed Wahib
,
Moncef Gabbouj
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.15600
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2407.15600
Neural architecture search (NAS) enables researchers to automatically explore vast search spaces and find efficient neural networks. But NAS suffers from a key bottleneck, i.e., numerous architectures need to be evaluated during the search process, which requires a lot of computing resources and time. In order to improve the efficiency of NAS, a series of methods have been proposed to reduce the evaluation time of neural architectures. However, they are not efficient enough and still only focus on the accuracy of architectures. In addition to the classification accuracy, more efficient and smaller network architectures are required in real-world applications. To address the above problems, we propose the SMEM-NAS, a pairwise comparison relation-assisted multi-objective evolutionary algorithm based on a multi-population mechanism. In the SMEM-NAS, a surrogate model is constructed based on pairwise comparison relations to predict the accuracy ranking of architectures, rather than the absolute accuracy. Moreover, two populations cooperate with each other in the search process, i.e., a main population guides the evolution, while a vice population expands the diversity. Our method aims to provide high-performance models that take into account multiple optimization objectives. We conduct a series of experiments on the CIFAR-10, CIFAR-100 and ImageNet datasets to verify its effectiveness. With only a single GPU searching for 0.17 days, competitive architectures can be found by SMEM-NAS which achieves 78.91% accuracy with the MAdds of 570M on the ImageNet. This work makes a significant advance in the important field of NAS. Our code is publicly available at https://github.com/ccz-enas/SMEM-NAS.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2407.15600
- https://arxiv.org/pdf/2407.15600
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402824313
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402824313Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2407.15600Digital Object Identifier
- Title
-
A Pairwise Comparison Relation-assisted Multi-objective Evolutionary Neural Architecture Search Method with Multi-population MechanismWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-07-22Full publication date if available
- Authors
-
Yu Xue, Chenchen Zhu, MengChu Zhou, Mohamed Wahib, Moncef GabboujList of authors in order
- Landing page
-
https://arxiv.org/abs/2407.15600Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2407.15600Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2407.15600Direct OA link when available
- Concepts
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Relation (database), Mechanism (biology), Pairwise comparison, Architecture, Computer science, Population, Artificial intelligence, Data mining, Geography, Physics, Sociology, Demography, Archaeology, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy, | 87 |
| abstract_inverted_index.accuracy. | 144 |
| abstract_inverted_index.algorithm | 114 |
| abstract_inverted_index.available | 245 |
| abstract_inverted_index.computing | 40 |
| abstract_inverted_index.cooperate | 148 |
| abstract_inverted_index.efficient | 14, 71, 89 |
| abstract_inverted_index.evaluated | 30 |
| abstract_inverted_index.important | 237 |
| abstract_inverted_index.networks. | 16 |
| abstract_inverted_index.problems, | 103 |
| abstract_inverted_index.relations | 132 |
| abstract_inverted_index.resources | 41 |
| abstract_inverted_index.searching | 206 |
| abstract_inverted_index.surrogate | 124 |
| abstract_inverted_index.comparison | 110, 131 |
| abstract_inverted_index.diversity. | 169 |
| abstract_inverted_index.efficiency | 49 |
| abstract_inverted_index.evaluation | 62 |
| abstract_inverted_index.evolution, | 162 |
| abstract_inverted_index.mechanism. | 119 |
| abstract_inverted_index.population | 159, 166 |
| abstract_inverted_index.real-world | 97 |
| abstract_inverted_index.bottleneck, | 23 |
| abstract_inverted_index.competitive | 210 |
| abstract_inverted_index.constructed | 127 |
| abstract_inverted_index.experiments | 189 |
| abstract_inverted_index.objectives. | 183 |
| abstract_inverted_index.populations | 147 |
| abstract_inverted_index.researchers | 5 |
| abstract_inverted_index.significant | 233 |
| abstract_inverted_index.architecture | 1 |
| abstract_inverted_index.evolutionary | 113 |
| abstract_inverted_index.optimization | 182 |
| abstract_inverted_index.applications. | 98 |
| abstract_inverted_index.architectures | 26, 93, 211 |
| abstract_inverted_index.automatically | 7 |
| abstract_inverted_index.architectures, | 139 |
| abstract_inverted_index.architectures. | 66, 81 |
| abstract_inverted_index.classification | 86 |
| abstract_inverted_index.effectiveness. | 200 |
| abstract_inverted_index.multi-objective | 112 |
| abstract_inverted_index.high-performance | 175 |
| abstract_inverted_index.multi-population | 118 |
| abstract_inverted_index.relation-assisted | 111 |
| abstract_inverted_index.https://github.com/ccz-enas/SMEM-NAS. | 247 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile |