MO-SMAC:基于多目标序列模型的算法配置
Jeroen G Rook1, Carolin Benjamins2, Jakob Bossek3
1Data Management & Biometrics, University of Twente, The Netherlands j.g.rook@utwente.nl.
Evolutionary computation
|March 17, 2025
概括
本研究介绍了一种新的多目标自动化算法配置器,以找到复杂任务的最佳AI参数. 它通过接近帕雷托设定的多个目标来增强可靠和资源高效的AI.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 优化优化 优化优化
背景情况:
- 自动化算法配置 (AAC) 传统上专注于单个目标.
- 现实世界的人工智能任务往往涉及多个潜在的相互冲突的绩效目标.
- 人们越来越需要可靠和资源高效的AI系统,需要多目标优化.
研究的目的:
- 开发一个通用的多目标自动化算法配置器.
- 扩展广泛使用的SMAC框架,用于多目标优化.
- 搜索一个接近真帕雷托集合的非主导集合.
主要方法:
- 提出了一种纯粹的多目标贝叶斯优化方法.
- 使用预测的超量改进作为获取函数.
- 引入了一种新的强化程序,用于高效的多目标配置选择.
主要成果:
- 在经验上验证了在四个AI领域的方法.
- 与基线方法相比,证明了更高的性能.
- 在特定场景中与MO-ParamILS实现了竞争力,显示了整体最佳表现.
结论:
- 拟议的多目标配置器有效地处理复杂的AI优化任务.
- 该方法使自动算法配置领域向更现实的多目标场景发展.
- 这项工作有助于开发更可靠,更有效的AI系统.
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