Related papers: Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method

Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method

URL: http://arxiv.org/abs/2207.03842v1
Date: Fri, 8 Jul 2022 11:51:48 GMT
Title: Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method
Authors: Bruno Barracosa (L2S, GdR MASCOT-NUM), Julien Bect (L2S, GdR MASCOT-NUM), H\'elo\"ise Dutrieux Baraffe, Juliette Morin, Josselin Fournel, Emmanuel Vazquez (L2S, GdR MASCOT-NUM)
Abstract summary: This article focuses on the multi-objective optimization of simulators with high output variance. We rely on Bayesian optimization algorithms to make predictions about the functions to be optimized.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: This article focuses on the multi-objective optimization of stochastic simulators with high output variance, where the input space is finite and the objective functions are expensive to evaluate. We rely on Bayesian optimization algorithms, which use probabilistic models to make predictions about the functions to be optimized. The proposed approach is an extension of the Pareto Active Learning (PAL) algorithm for the estimation of Pareto-optimal solutions that makes it suitable for the stochastic setting. We named it Pareto Active Learning for Stochastic Simulators (PALS). The performance of PALS is assessed through numerical experiments over a set of bi-dimensional, bi-objective test problems. PALS exhibits superior performance when compared to other scalarization-based and random-search approaches.