Results 1 to 10 of about 288,541 (88)

Scalarizing Functions in Bayesian Multiobjective Optimization [PDF]

, 2019
Scalarizing functions have been widely used to convert a multiobjective optimization problem into a single objective optimization problem. However, their use in solving (computationally) expensive multi- and many-objective optimization problems in ...
Chugh, Tinkle
core   +2 more sources

Bayesian optimization for computationally extensive probability distributions [PDF]

, 2018
An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique.
Hukushima, Koji, Tamura, Ryo
core   +1 more source

Bayesian Inference in Estimation of Distribution Algorithms [PDF]

, 2007
Metaheuristics such as Estimation of Distribution Algorithms and the Cross-Entropy method use probabilistic modelling and inference to generate candidate solutions in optimization problems.
Gallagher, Marcus, Keith, Jonathan, Sofronov, George, Wood, Ian   +3 more
core   +3 more sources

Unscented Bayesian Optimization for Safe Robot Grasping [PDF]

, 2016
We address the robot grasp optimization problem of unknown objects considering uncertainty in the input space. Grasping unknown objects can be achieved by using a trial and error exploration strategy.
Bernardino, Alexandre, Jamone, Lorenzo, Martinez-Cantin, Ruben, Nogueira, José   +3 more
core   +2 more sources

A Bayesian approach to constrained single- and multi-objective optimization [PDF]

, 2016
This article addresses the problem of derivative-free (single- or multi-objective) optimization subject to multiple inequality constraints. Both the objective and constraint functions are assumed to be smooth, non-linear and expensive to evaluate.
A Bayesian, Emmanuel Vazquez, Emmanuel Vazquez, Hal Id Hal, Julien Bect, Julien Bect, Paul Feliot, Paul Feliot   +7 more
core   +5 more sources

Global optimization of complex optical structures using Baysian optimization based on Gaussian processes

, 2017
Numerical simulation of complex optical structures enables their optimization with respect to specific objectives. Often, optimization is done by multiple successive parameter scans, which are time consuming and computationally expensive.
Burger, S., Rockstuhl, C., Santiago, X. Garcia, Schneider, P. -I.   +3 more
core   +1 more source

Bayesian optimization for materials design

, 2015
We introduce Bayesian optimization, a technique developed for optimizing time-consuming engineering simulations and for fitting machine learning models on large datasets.
A Booker, A Forrester, AB Gelman, AIJ Forrester, B Ankenman, BE Stuckman, CE Rasmussen, D Huang, D Huang, David Ginsbourger, Diana M. Negoescu, DR Jones, HJ Kushner, J Bect, J Knowles, J Mockus, J Villemonteix, J Xie, LP Kaelbling, Noel Cressie, PI Frazier, PI Frazier, PI Frazier, R Waeber, RA Howard, RS Sutton, Sethuraman Sankaran, TJ Santner, W Scott   +28 more
core   +1 more source

Bayesian optimization in ab initio nuclear physics [PDF]

, 2019
Theoretical models of the strong nuclear interaction contain unknown coupling constants (parameters) that must be determined using a pool of calibration data.
Dimitrakakis, C., Dubhashi, D., Ekström, A., Forssén, C., Johansson, H. T., Muhammad, A. S., Salomonsson, H., Schliep, A.   +7 more
core   +3 more sources

Using Gaussian process regression for efficient parameter reconstruction

, 2019
Optical scatterometry is a method to measure the size and shape of periodic micro- or nanostructures on surfaces. For this purpose the geometry parameters of the structures are obtained by reproducing experimental measurement results through numerical ...
Chisari, Golovin, Hammerschmidt, Hammerschmidt, Potzick, Rasmussen, Santiago, Schneider, Schneider, Scholze, Shahriari, Sobol’, Solak   +12 more
core   +1 more source

Determination of the quark-gluon string parameters from the data on pp, pA and AA collisions at wide energy range using Bayesian Gaussian Process Optimization

, 2019
Bayesian Gaussian Process Optimization can be considered as a method of the determination of the model parameters, based on the experimental data.
Kovalenko, Vladimir
core   +1 more source