一个高效的进化神经架构搜索算法,没有训练
Yang An1, Changsheng Zhang1,2, Jintao Shao3
1Software College, Northeastern University, Shenyang 110167, China.
Biomimetics (Basel, Switzerland)
|July 25, 2025
概括
这项研究介绍了一种高效的进化神经架构搜索 (ENAS) 方法. 它通过增强进化算法和使用无培训的评估器来加速网络架构的发现,大大减少了搜索时间和计算成本.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 神经架构搜索 (NAS) 自动化了高性能神经网络的设计.
- 现有的NAS方法面临挑战,因为性能评估需要大量的时间和计算资源.
研究的目的:
- 提出一种高效的进化神经架构搜索 (ENAS) 方法.
- 解决NAS中的时间和计算成本挑战.
- 为了加速融合速度并缩短NAS算法中的搜索时间.
主要方法:
- 重新设计基于生物识别原则的进化算法交互,以增强信息交换和优化.
- 引入了一个多度指标,无需培训的评估器来评估网络性能,绕过资源密集型培训.
- 使用NAS-Bench-101和NAS-Bench-201基准进行评估.
主要成果:
- 拟议的ENAS方法证明了改进的本地和全球搜索能力.
- 无需培训的多度指标评估员有效评估绩效,并减轻排名偏移问题.
- 与最先进的方法相比,识别了具有可比或优越性能的网络架构.
结论:
- 该ENAS方法显著减少了NAS所需的时间和计算资源.
- 该方法为自动神经网络架构设计提供了更高效和有效的解决方案.
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