To achieve peak predictive performance, hyperparameter optimization (HPO) is a crucial
component of machine learning and its applications. Over the last years, the number of …

Designing optimization approaches, whether heuristic or meta-heuristic, usually demands
extensive manual intervention and has difficulty generalizing across diverse problem …

The growing ubiquity of machine learning (ML) has led it to enter various areas of computer
science, including black-box optimization (BBO). Recent research is particularly concerned …

This study focuses on the locomotion capability improvement in a tendon-driven soft
quadruped robot through an online adaptive learning approach. Leveraging the inverse …

In the context of the ever-evolving 5G landscape, where network management and control
are paramount, a new Radio Access Network (RAN) as emerged. This innovative RAN offers …

Surrogate-assisted evolutionary algorithms (SAEAs) are recently among the most widely
studied methods for their capability to solve expensive real-world optimization problems …

Ozone-based processes involve complex reaction networks and exhibit matrix-dependent
decomposition patterns when dosed to wastewater effluents. Existing models for ozone …

Optimizing parameters of physics-based simulators is crucial in the design process of
engineering and scientific systems. This becomes particularly challenging when the …

The growing ubiquity of machine learning (ML) has led it to enter various areas of computer
science, including black-box optimization (BBO). Recent research is particularly concerned …

Surrogate-assisted evolutionary algorithms (SAEAs) are currently among the most widely
researched techniques for their capability to solve expensive real-world optimization …