Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …

Big Data applications are typically associated with systems involving large numbers of
users, massive complex software systems, and large-scale heterogeneous computing and …

Bayesian optimization provides sample-efficient global optimization for a broad range of
applications, including automatic machine learning, engineering, physics, and experimental …

Computer simulation experiments are essential to modern scientific discovery, whether that
be in physics, chemistry, biology, epidemiology, ecology, engineering, etc. Surrogates are …

In many engineering optimization problems, the number of function evaluations is severely
limited by time or cost. These problems pose a special challenge to the field of global …

WEKA is a widely used, open-source machine learning platform. Due to its intuitive interface,
it is particularly popular with novice users. However, such users often find it hard to identify …

Many different machine learning algorithms exist; taking into account each algorithm's
hyperparameters, there is a staggeringly large number of possible alternatives overall. We …

This paper presents a taxonomy of existing approaches for using response surfaces for
global optimization. Each method is illustrated with a simple numerical example that brings …

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 …

Computation-intensive design problems are becoming increasingly common in
manufacturing industries. The computation burden is often caused by expensive analysis …