Initialization Matters for Asynchronous Steady-State Evolutionary Algorithms
Author:
Affiliation:
1. MITRE Corporation, McLean, United States of America
2. George Mason University, Fairfax, United States of America
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3583133.3596404
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3. Zheng Chai , Yujing Chen , Liang Zhao , Yue Cheng , and Huzefa Rangwala . 2020 . Fedat: A communication-efficient federated learning method with asynchronous tiers under non-iid data. arXiv preprint arXiv:2010.05958 (2020). Zheng Chai, Yujing Chen, Liang Zhao, Yue Cheng, and Huzefa Rangwala. 2020. Fedat: A communication-efficient federated learning method with asynchronous tiers under non-iid data. arXiv preprint arXiv:2010.05958 (2020).
4. Asynchronous Online Federated Learning for Edge Devices with Non-IID Data
5. A partially asynchronous global parallel genetic algorithm
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