Probabilistic Smoothing with Ratio-Monotone Transforms for Global Optimization

ArXi:2605.27316v1 Announce Type: new Probabilistic smoothing is a standard tool for global optimization, but existing methods rely on Gaussian kernels and specific transforms, often resulting in strong hyperparameter sensitivity and limited robustness. We propose a general smoothing framework that combines flexible symmetric unimodal kernels with monotonic ratio-based transformations.