New Bayesian optimization algorithm using a sequential Monte-Carlo approach
type: Dataset
description: This thesis deals with the problem of global optimization of expensive-to-evaluate functions in a Bayesian framework. We say that a function is expensive-to-evaluate when its evaluation requires a significant amount of resources (e.g., very long numerical simulations).In this context, it is important to use optimization algorithms that can deal with a limited number of function evaluations. We consider here a Bayesian approach which consists in assigning a prior to the function, under the form of a Gaussian random process. The idea is then to choose the next evaluation points using a probabilistic criterion that indicates, conditional on the previous evaluations, the most interesting regions of the research domain for the optimizer. Two difficulties in this approach can be identified: the choice of the Gaussian process prior and the maximization of the criterion. The first problem is usually solved by using a maximum likelihood approach, which turns out to be a poorly robust method, and to which we prefer a fully Bayesian approach. The contribution of this work is the introduction of a new Bayesian optimization algorithm, which maximizes the Expected Improvement (EI) criterion, and provides an answer to both problems thanks to a Sequential Monte Carlo approach. Numerical results on benchmark tests show good performances of our algorithm compared to those of several other methods of the literature.
landing page: https://theses.hal.science/tel-00864700
identifier: NNT: 2013SUPL0011
keyword: critere-ei
keyword: expected-improvement-criterion
keyword: gaussian-process
keyword: infoeu-reposemanticsdoctoralthesis
keyword: krigeage
keyword: kriging
keyword: methodes-smc
keyword: optimisation
keyword: optimization
keyword: processus-gaussien
keyword: smc-methods
keyword: spiotherengineering-sciences-physicsother
keyword: theses
issued: 2026-05-09T15:54:25.446472
modified: 2026-05-09T15:54:25.446477
language: fr
contact point: Organization
  fn: CCSD

test_moissonnage_selune
type: Agent

New Bayesian optimization algorithm using a sequential Monte-Carlo approach
type: Distribution
format: HTML
access URL: https://theses.hal.science/tel-00864700
issued: 2026-05-09T15:54:25.454275
modified: 2026-05-09T15:54:25.434708