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This chapter briefly explains the Bayesian optimization algorithm, specifically looking at it as a learning-and-optimization framework, under uncertainty. Then, the Gaussian process-based Bayesian optimization algorithm is detailed, along with its extension to the multiple information source optimization setting. The necessary distinctions with multi-fidelity optimization are introduced. Finally, relations with other closely related settings are also discussed, specifically multitask and multi-objective optimization.

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Introduction

  • 章节

  • 发表日期：

    2025年

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  • Antonio Candelieri,
  • Andrea Ponti,
  • Francesco Archetti

摘要

This chapter briefly explains the Bayesian optimization algorithm, specifically looking at it as a learning-and-optimization framework, under uncertainty. Then, the Gaussian process-based Bayesian optimization algorithm is detailed, along with its extension to the multiple information source optimization setting. The necessary distinctions with multi-fidelity optimization are introduced. Finally, relations with other closely related settings are also discussed, specifically multitask and multi-objective optimization.