Machine–Learning in Optimization of Expensive Black

Blackbox: A procedure for parallel optimization of expensive black-box functions

This note provides a description of a procedure that is designed to efficiently optimize expensive black-box functions. It uses the response surface methodology by incorporating radial basis functions as the response model. A simple method based on a Latin hypercube is used for initial sampling. A modified version of CORS algorithm with space rescaling is used for the subsequent sampling. The p...

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Parallel Bayesian Global Optimization of Expensive Functions

We consider parallel global optimization of derivative-free expensive-to-evaluate functions, and proposes an efficient method based on stochastic approximation for implementing a conceptual Bayesian optimization algorithm proposed by [10]. To accomplish this, we use infinitessimal perturbation analysis (IPA) to construct a stochastic gradient estimator and show that this estimator is unbiased.

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Bayesian Monte Carlo for the Global Optimization of Expensive Functions

In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long running times of such models a promising approach has been to replace them by stochastic approximations based on a few model evaluations. In this paper we focus on the often occuring case that the system mod elled has t...

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Bayesian Monte Carlo for the Global Optimization of Expensive Functions

In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long running times of such models a promising approach has been to replace them by stochastic approximations based on a few model evaluations. In this paper we focus on the often occuring case that the system mod elled has t...

متن کامل

Bayesian Monte Carlo for the Global Optimization of Expensive Functions

In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long running times of such models a promising approach has been to replace them by stochastic approximations based on a few model evaluations. In this paper we focus on the often occuring case that the system mod elled has t...

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Machine–Learning in Optimization of Expensive Black–Box Functions

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منابع مشابه

Blackbox: A procedure for parallel optimization of expensive black-box functions

Parallel Bayesian Global Optimization of Expensive Functions

Bayesian Monte Carlo for the Global Optimization of Expensive Functions

Bayesian Monte Carlo for the Global Optimization of Expensive Functions

Bayesian Monte Carlo for the Global Optimization of Expensive Functions

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