使用机器学习方法来评估模块优化框架中的模块性能贡献
Ana Kostovska1, Diederick Vermetten2, Peter Korošec3
1Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, 1000, Slovenia ana.kostovska@ijs.si.
Evolutionary computation
|August 5, 2024
概括
模块化算法框架揭示了关键性能驱动因素. 协差矩阵适应进化策略 (CMA-ES) 中的精英化模块和差异进化 (DE) 中的线性人口规模减少显著影响表现.
科学领域:
- 计算科学 计算科学
- 优化算法 优化算法
- 机器学习 机器学习
背景情况:
- 模块化算法框架提供了对算法组件的结构化分析.
- 评估单个模块对整体性能的影响对于算法设计至关重要.
研究的目的:
- 提出和验证一种方法来分析模块对无衍生品黑盒优化算法的影响.
- 确定影响协差矩阵适应演化策略 (CMA-ES) 和差异演化 (DE) 的关键模块.
主要方法:
- 分析了来自324个modCMA-ES和576个modDE变体的性能数据,涉及24个BBOB问题和6个运行时间预算.
- 探索性数据分析问题景观特征及其对算法性能的影响.
- 应用基于特征重要性的分类器来预测模块化配置.
主要成果:
- 单个模块对整体算法性能影响的显著变化.
- 在CMA-ES的精英主义模块和DE的线性人口规模减少被确定为具有最显著的影响.
- 景观特征对性能的影响因模块配置而异,尽管相关特征保持一致.
结论:
- 该研究提供了一种方法来剖析基于模块化组件的算法性能.
- 关键模块显著影响优化性能,指导未来的算法设计.
- 模块化配置的预测模型显示了与真实配置可比的性能,验证了该方法.
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