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

The expected improvement (EI) algorithm is a very popular method for expensive
optimization problems. In the past twenty years, the EI criterion has been extended to deal …

Parameter tuning, that is, to find appropriate parameter settings (or configurations) of
algorithms so that their performance is optimized, is an important task in the development …

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a
growing research topic with many competitive bio-inspired algorithms being proposed every …

“Exploration and exploitation are the two cornerstones of problem solving by search.” For
more than a decade, Eiben and Schippers' advocacy for balancing between these two …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

State-of-the-art algorithms for hard computational problems often expose many parameters
that can be modified to improve empirical performance. However, manually exploring the …

We present a tutorial on Bayesian optimization, a method of finding the maximum of
expensive cost functions. Bayesian optimization employs the Bayesian technique of setting …

Deep neural networks are commonly trained using stochastic non-convex optimization
procedures, which are driven by gradient information estimated on fractions (batches) of the …

This survey compiles ideas and recommendations from more than a dozen researchers with
different backgrounds and from different institutes around the world. Promoting best practice …