对于神经架构的搜索,一种选择性扰动式超启发式
Johannes De Clercq1, Nelishia Pillay1
1Department of Computer Science, University of Pretoria, Pretoria, 0028, Gauteng, South Africa.
概括
本研究引入了一种新的超启发式方法,用于神经架构搜索 (NAS),通过启发式空间间接探索设计空间. 这种方法,SPHH-NAS,性能优于现有的技术,并降低了计算成本.
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) 传统上直接探索设计空间.
- 像遗传算法这样的现有方法在探索完整的架构频谱时面临限制.
- 需要使用替代搜索策略来克服这些局限性.
研究的目的:
- 通过探索启发式空间来研究NAS的替代方法.
- 引入一种超启发式方法来间接搜索设计空间.
- 为了评估这种间接搜索策略的有效性.
主要方法:
- 介绍了NAS操作员空间 (NOS).
- 为NAS开发一个单点选择扰乱式超启发式NAS (SPHH-NAS).
- 使用选择函数进行启发式选择和适应性改进有限目标接受 (AILTA) 进行移动接受.
主要成果:
- SPHH-NAS成功地探索了启发空间,间接地映射到NOS,然后是设计空间.
- 该方法在NAS-101,NAS-201和NAS-301基准集上表现优于大多数以前的方法.
- 在两个真实世界数据集上的评估证实了SPHH-NAS的有效性.
- 通过SPHH-NAS.观察到计算成本的显著降低.
结论:
- 通过启发式空间间接搜索设计空间可以访问尚未探索的区域.
- SPHH-NAS为NAS中直接搜索方法提供了一个更有效和更有效的替代方案.
- 拟议的方法通过提高性能和减少计算需求来推进自动机器学习领域.
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