Parallel Sequential Random Embedding Bayesian Optimization
نویسندگان
چکیده
منابع مشابه
The Parallel Bayesian Optimization Algorithm
In the last few years there has been a growing interest in the field of Estimation of Distribution Algorithms (EDAs), where crossover and mutation genetic operators are replaced by probability estimation and sampling techniques. The Bayesian Optimization Algorithm incorporates methods for learning Bayesian networks and uses these to model the promising solutions and generate new ones. The aim o...
متن کاملBayesian Optimization Using Sequential Monte Carlo
We consider the problem of optimizing a real-valued continuous function f using a Bayesian approach, where the evaluations of f are chosen sequentially by combining prior information about f , which is described by a random process model, and past evaluation results. The main difficulty with this approach is to be able to compute the posterior distributions of quantities of interest which are u...
متن کاملParallel Processing in Sequential Approximate Optimization
The paper presents a first level of coarse-grained parallelization in a sequential approximate optimization framework. A sequential approximate optimization framework builds local approximations of the system every iteration by evaluating a set of design points around the current design. In this research the database is generated by distributing the data sampling process among several processor...
متن کاملParallel 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.
متن کاملMassively Parallel Sequential Monte Carlo for Bayesian Inference
This paper reconsiders sequential Monte Carlo approaches to Bayesian inference in the light of massively parallel desktop computing capabilities now well within the reach of individual academics. It first develops an algorithm that is well suited to parallel computing in general and for which convergence results have been established in the sequential Monte Carlo literature but that tends to re...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SN Computer Science
سال: 2020
ISSN: 2662-995X,2661-8907
DOI: 10.1007/s42979-020-00385-8