A Bayesian approach to constrained single- and multi-objective optimization

The optimization of the criterion is also a difficult problem because the EI criterion is known to be highly multi-modal and hard to optimize. Our proposal is to conduct

proposed an exact solution method based on the difference of convex func- tions to solve cardinality constrained portfolio optimization problems.. To our knowledge, all existing

/ La version de cette publication peut être l’une des suivantes : la version prépublication de l’auteur, la version acceptée du manuscrit ou la version de l’éditeur. For

Such a process is based on the design of experi- ments with associated modeling methods, in order to reduce the number of tests used to build engine response models depending on

strategies are iterative and based on two steps: a NSGA-II algorithm performed on kriging response surfaces or kriging expected improvements and relevant enrichment methods composed

To solve the robust optimization problem, one way is to introduce a multi-objective optimization formulation where the first objective is the function itself and the second is

In a general way, according to the performance metrics used, IBEA avg glob- ally outperforms IBEA 1 and IBEA stoch for all probability distributions (except for the first

We address the problem of derivative-free multi-objective optimization of real-valued functions subject to multiple inequality constraints.. The search domain X is assumed to