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Surrogate Model Development for Digital Experiments in Welding
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在进化黑盒背景下替代模型的景观分析.
Zbyněk Pitra1, Jan Koza2, Jiří Tumpach3
1Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University Břehová 7, 115 19 Prague, Czech Republic z.pitra@gmail.com.
Evolutionary computation
|August 14, 2024
概括
这项研究探讨了替代模型准确性如何与优化期间的黑子函数景观有关. 这些发现旨在指导替代模型的自动选择和调整,以提高性能.
科学领域:
- 优化优化 优化优化
- 机器学习 机器学习
- 计算科学 计算科学
背景情况:
- 替代模型对于昂贵的黑子优化至关重要.
- 协差矩阵适应演化策略 (CMA-ES) 是此类任务的领先优化器.
- 在优化中理解代用模型行为是提高效率的关键.
研究的目的:
- 调查代理模型预测准确性,它们的配置和黑子功能景观特征之间的联系.
- 为自动化代孕模型选择和调整策略奠定基础.
- 在优化过程中分析模型错误对景观特征的依赖性.
主要方法:
- 特征分析从最初的384.4中确定了14个相关的景观特征.
- 在39个设置中探索了四个替代模型的错误依赖性.
- 在代理辅助的CMA-ES.中使用了三种输入数据选择方法.
- 通过使用比较连续优化器 (COCO) 框架对无噪声基准进行了实验.
主要成果:
- 确定了重要的非坚固的特征,并将类似的景观特征聚集在一起.
- 发现了景观特征和替代模型错误之间的特定关系.
- 模型错误依赖性因设置和输入数据选择方法而异.
结论:
- 这项研究为进化优化中的代孕模型行为提供了见解.
- 结果可以为选择和调整代孕模型的适应性策略的开发提供信息.
- 了解景观模型的相互作用对于优化昂贵的黑子问题至关重要.
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