深度ELA:深度探索性景观分析与自主监督的预训练变压器,用于单个和多个目标的持续优化问题
Moritz Vinzent Seiler1, Pascal Kerschke2,3, Heike Trautmann4,5
1Machine Learning and Optimisation, Paderborn University, Germany moritz.seiler@uni-paderborn.de.
Evolutionary computation
|February 4, 2025
概括
本研究介绍了Deep-ELA,这是一种混合方法,结合了深度学习和探索性景观分析 (ELA) 功能. 深度ELA有效地描述了单一和多目标优化问题,克服了传统ELA方法的局限性.
科学领域:
- 人工智能的人工智能
- 优化优化 优化优化
- 机器学习 机器学习
背景情况:
- 探索性景观分析 (ELA) 功能从数值上描述单一目标的持续优化问题,帮助机器学习任务,如算法选择和配置.
- 传统的ELA特征表现出强烈的相关性和有限的适用于多目标优化问题.
- 深度学习方法,如点云转换器,已被提出为替代方案,但需要大量的标记训练数据.
研究的目的:
- 提出一个混合框架,Deep-ELA,将深度学习与ELA功能集成在一起.
- 解决现有的ELA方法的缺点,特别是它们在多目标优化和特征相关性方面的局限性.
- 开发一种方法来描述单个和多个目标的连续优化问题,使用深度表示.
主要方法:
- 开发了一种混合方法,Deep-ELA,将深度学习和ELA功能结合起来.
- 在数百万个随机生成的优化问题上预先训练了四个变压器.
- 对于连续的单一和多目标优化问题,学习了健身景观的深度表示.
主要成果:
- 成功预训练变压器以生成优化问题景观的深度表示.
- 深度ELA框架在分析单个和多个目标的持续优化问题方面表现出有效性.
- 该框架可以用于开箱即用或微调用于与算法行为和问题理解相关的特定任务.
结论:
- 深度ELA提供了一种强大的混合方法,利用深度学习和ELA功能来克服传统方法的局限性.
- 预训练过的变压器提供了强大的深度表示,用于描述复杂的优化场景.
- 该框架增强了对持续优化问题的分析和理解,有可能在算法选择和配置中广泛应用.
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