Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Optimizing expensive-to-evaluate black-box functions of discrete (and potentially
continuous) design parameters is a ubiquitous problem in scientific and engineering …

Algorithms for machine learning have found extensive use in numerous fields and
applications. One important aspect of effectively utilizing these algorithms is tuning the …

Many real-world problems can be phrased as a multi-objective optimization problem, where
the goal is to identify the best set of compromises between the competing objectives. Multi …

We consider the problem of optimizing combinatorial spaces (eg, sequences, trees, and
graphs) using expensive black-box function evaluations. For example, optimizing molecules …

Thermal energy storage (TES) has been widely applied in buildings to shift air-conditioning
peak loads and to reduce operating costs by using time-of-use (ToU) tariffs. Meanwhile, TES …

Efficient optimisation of black-box problems that comprise both continuous and categorical
inputs is important, yet poses significant challenges. Current approaches, like one-hot …

At the basic level, groundwater potential zone (GWPZ) map** plays an important role in
sustainable water resource management. There are different approaches to delineating …

As parallel applications become more complex, auto-tuning becomes more desirable,
challenging, and time-consuming. We propose, Bliss, a novel solution for auto-tuning …

The rapid development of novel synthetic routes for pharmaceutical compounds is highly
attractive for overcoming pandemic and epidemic-prone diseases like COVID-19. Herein …