本文深入探讨了贝叶斯多目标优化的偏好塑造预期改进标准，考察了两个指标家族：超体积和R2。它精确定义了哪些偏好变换能保留计算属性，哪些会改变底层几何形状。该研究阐明了精确积分R2改进与目标空间加权超体积之间的关系，并提出了离散和积分R2改进的新算法方法。 AI
影响 这项研究改进了优化算法的理论基础，可能影响未来AI模型的训练和开发。
排序理由 该集群包含一篇详细介绍优化标准理论进展的学术论文。
在 arXiv cs.NE (Neural & Evolutionary) 阅读 →
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arXiv:2605.28746v1 Announce Type: cross Abstract: This paper studies preference-shaped expected improvement criteria for Bayesian multiobjective optimization. We consider two indicator families which are often used for similar algorithmic purposes, but which are geometrically dif…
This paper studies preference-shaped expected improvement criteria for Bayesian multiobjective optimization. We consider two indicator families which are often used for similar algorithmic purposes, but which are geometrically different. The hypervolume indicator is based on a dy…
This paper studies preference-shaped expected improvement criteria for Bayesian multiobjective optimization. We consider two indicator families which are often used for similar algorithmic purposes, but which are geometrically different. The hypervolume indicator is based on a dy…