Iterative Kriging-Based Methods for Expensive Black-Box Models

The use of iterative methods for solving Black-Scholes equation

Surrogate-based methods for black-box optimization

In this paper, we survey methods that are currently used in black-box optimization, i.e. the kind of problems whose objective functions are very expensive to evaluate and no analytical or derivative information are available. We concentrate on a particular family of methods, in which surrogate (or meta) models are iteratively constructed and used to search for global solutions.

A Survey Of Methods For Explaining Black Box Models

In the last years many accurate decision support systems have been constructed as black boxes, that is as systems that hide their internal logic to the user. This lack of explanation constitutes both a practical and an ethical issue. The literature reports many approaches aimed at overcoming this crucial weakness sometimes at the cost of scarifying accuracy for interpretability. The application...

Efficient Global Optimization of Expensive Black-Box Functions

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 optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective ...

A kriging based method for the solution of mixed-integer nonlinear programs containing black-box functions

In this paper a new methodology is developed for the solution of mixed-integer nonlinear programs under uncertainty whose problem formulation is complicated by both noisy variables and black-box functions representing a lack of model equations. A branchand-bound framework is employed to handle the integer complexity whereby the solution to the relaxed nonlinear program subproblem at each node i...