基于人口的变异减小进化比随机的景观
Zelin Pei1, Xiaoyu He2, Yi Pan1
1School of Software Engineering, Sun Yat-Sen University, Guangzhou, 510006, China.
Scientific reports
|October 7, 2025
概括
我们介绍了基于人口的变异减少进化 (PVRE),这是一种新的优化方法,可以减少解决方案和数据空间中的噪音. 对于零阶优化问题,PVRE实现了最先进的融合.
科学领域:
- 优化优化 优化优化
- 机器学习 机器学习
- 进化计算是一种进化计算.
背景情况:
- 黑盒随机优化需要在解决方案和数据空间中采样.
- 传统方法在解决方案空间噪声较高时,难以实现融合.
研究的目的:
- 为了介绍一种新的零级优化方法,基于人口的变异减小进化 (PVRE).
- 在解决方案和数据空间中同时减轻噪声,以改善融合.
主要方法:
- 对于数据采样和噪声降低,PVRE采用了规范动量的机制.
- 包含基于人口的梯度估计方案,以减少解决方案空间噪声.
主要成果:
- PVRE 证明了理论上的收性质和进化算法的适应性.
- 实现了最知名的函数评估复杂度,以找到 ε-准确的第一阶最佳解决方案.
结论:
- PVRE为黑盒随机优化提供了一种有效的方法.
- 通过对基准问题和对神经图像分类器的对抗性攻击的实验来验证.
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