Articlegold2020

Bayesian Optimization for Adaptive Experimental Design: A Review

Stewart Greenhill · Santu Rana · Sunil Gupta · Pratibha Vellanki · Svetha Venkatesh

525 citations0/0 files storedIEEE Access

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Optimal Experimental Design Methods Advanced Multi-Objective Optimization Algorithms Advanced Bandit Algorithms Research

Abstract

Bayesian optimisation is a statistical method that efficiently models and optimises expensive “black-box” functions. This review considers the application of Bayesian optimisation to experimental design, in comparison to existing Design of Experiments (DOE) methods. Solutions are surveyed for a range of core issues in experimental design including: the incorporation of prior knowledge, high dimensional optimisation, constraints, batch evaluation, multiple objectives, multi-fidelity data, and mixed variable types.

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References

1996Response Surface Methodology: Process and Product Optimization Using Designed Experiments 1989Design and Analysis of Computer Experiments 2012Practical Bayesian Optimization of Machine Learning Algorithms 2015Taking the Human Out of the Loop: A Review of Bayesian Optimization 1951On the Experimental Attainment of Optimum Conditions 2017Design and Analysis of Experiments 2012Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting 2010A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement Learning

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2021Design of Experiments and machine learning for product innovation: A systematic literature review 2022Beyond playing 20 questions with nature: Integrative experiment design in the social and behavioral sciences 2021Bayesian Optimization in High-Dimensional Spaces: A Brief Survey 2022Beyond Playing 20 Questions with Nature: Integrative Experiment Design in the Social and Behavioral Sciences 2025Bayesian Optimization in Bioprocess Engineering—Where Do We Stand Today?

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