Automation is playing an increasingly significant role in synthetic biology. Groundbreaking
technologies, developed over the past 20 years, have enormously accelerated the …

In many optimization problems arising from scientific, engineering and artificial intelligence
applications, objective and constraint functions are available only as the output of a black …

In this introductory chapter, we present a high-level description of optimization, blackbox
optimization, and derivative-free optimization. We introduce some basic optimization …

This paper reviews the literature on algorithms for solving bound-constrained mixed-integer
derivative-free optimization problems and presents a systematic comparison of available …

We present two software packages for derivative-free optimization (DFO): DFO-LS for
nonlinear least-squares problems and Py-BOBYQA for general objectives, both with optional …

Do you know the difference between an optimist and a pessimist? The former believes we
live in the best possible world, and the latter is afraid that the former might be right.… In that …

Abstract The late Professor MJD Powell devised five trust-region methods for derivative-free
optimization, namely COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. He carefully …

The mesh adaptive direct search (Mads) algorithm is designed for blackbox optimization
problems for which the functions defining the objective and the constraints are typically the …

This thesis studies derivative-free optimization (DFO), particularly model-based methods
and software. These methods are motivated by optimization problems for which it is …

The performance of deep neural networks is highly sensitive to the choice of the
hyperparameters that define the structure of the network and the learning process. When …