Learning Enabled Constrained Black-Box Optimization

F. Archetti, A. Candelieri (), B. G. Galuzzi and R. Perego
Additional contact information
F. Archetti: University of Milano-Bicocca
A. Candelieri: University of Milano-Bicocca
B. G. Galuzzi: University of Milano-Bicocca
R. Perego: University of Milano-Bicocca

A chapter in Black Box Optimization, Machine Learning, and No-Free Lunch Theorems, 2021, pp 1-33 from Springer

Abstract: Abstract This chapter looks at the issue of black-box constrained optimization where both the objective function and the constraints are unknown and can only be observed pointwise. Both deterministic and probabilistic surrogate models are considered: the latter, more specifically analysed, are based on Gaussian Processes and Bayesian Optimization to handle the exploration–exploitation dilemma and improve sample efficiency. Particularly challenging is the case when the feasible region might be disconnected and the objective function cannot be evaluated outside the feasible region; this situation, known as “partially defined objective function” or “non-computable domains”, requires a novel approach: a first phase is based on the SVM classification in order to learn the feasible region, and a second phase, optimization, is based on a Gaussian Process. This approach is the main focus of this chapter that analyses modelling and computational issues and demonstrates the sample efficiency of the resulting algorithms.

Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-66515-9_1

Ordering information: This item can be ordered from
http://www.springer.com/9783030665159

DOI: 10.1007/978-3-030-66515-9_1

Access Statistics for this chapter

More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().