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515. A Bi-Objective Optimization Based Acquisition Strategy for Batch Bayesian Global Optimization
Invited abstract in session TB-37: Advances in Continuous Multiobjective Optimization, stream Multiobjective Optimization.
Tuesday, 10:30-12:00Room: 33 (building: 306)
Authors (first author is the speaker)
| 1. | Pierluigi Mansueto
|
| Department of Information Engineering, University of Florence | |
| 2. | Francesco Carciaghi
|
| University of Florence | |
| 3. | Simone Magistri
|
| Information Engineering (DINFO), University of Florence | |
| 4. | Fabio Schoen
|
| Dipartimento di Ingegneria dell'Informazione, Università degli Studi di Firenze |
Abstract
In this work, we deal with batch Bayesian Optimization problems over a box and we propose a novel bi-objective optimization (BOO) acquisition strategy to sample points where to evaluate the objective function. The BOO problem involves the Gaussian Process posterior mean and variance functions, which, in most of the acquisition strategies from the literature, are generally used in combination, frequently through scalarization. However, such scalarization could compromise the Bayes-Opt process performance, as getting the desired trade-off between exploration and exploitation is not trivial in most cases. We instead aim to reconstruct the Pareto front of the BOO problem based on optimizing both the posterior mean as well as the variance, thus generating multiple trade-offs without any a priori knowledge. We then propose two possible methodologies to select potentially optimal points from the Pareto front. Our methodology is finally tested with well-known acquisition strategies from the literature, in order to highlight its effectiveness on different settings.
Keywords
- Continuous Optimization
- Multi-Objective Decision Making
Status: accepted
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