# Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis

arXiv: https://arxiv.org/abs/2211.09678 · HTML: https://mlsanity.com/paper/2211.09678/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 27 references that are in the map.
- First posted on arXiv 17 Nov 2022.

## Builds on (most cited first, 12 of 12)

- [SMAC3: A Versatile Bayesian Optimization Package for Hyperparameter Optimization](https://mlsanity.com/paper/2109.09831/) (2021)
- [COCO: A Platform for Comparing Continuous Optimizers in a Black-Box Setting](https://mlsanity.com/paper/1603.08785/) (2016)
- [Automated Algorithm Selection: Survey and Perspectives](https://mlsanity.com/paper/1811.11597/) (2018)
- [Tuning Hyperparameters without Grad Students: Scalable and Robust Bayesian Optimisation with Dragonfly](https://mlsanity.com/paper/1903.06694/) (2019)
- [Improving the Expected Improvement Algorithm](https://mlsanity.com/paper/1705.10033/) (2017)
- [Comprehensive Feature-Based Landscape Analysis of Continuous and Constrained Optimization Problems Using the R-Package flacco](https://mlsanity.com/paper/1708.05258/) (2017)
- [Per-run Algorithm Selection with Warm-starting using Trajectory-based Features](https://mlsanity.com/paper/2204.09483/) (2022)
- [Towards Assessing the Impact of Bayesian Optimization's Own Hyperparameters](https://mlsanity.com/paper/1908.06674/) (2019)
- [Towards Feature-Based Performance Regression Using Trajectory Data](https://mlsanity.com/paper/2102.05370/) (2021)
- [Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration](https://mlsanity.com/paper/2202.03259/) (2022)
- [Initial Design Strategies and their Effects on Sequential Model-Based Optimization](https://mlsanity.com/paper/2003.13826/) (2020)
- [Trajectory-based Algorithm Selection with Warm-starting](https://mlsanity.com/paper/2204.06397/) (2022)

## Built on by (most cited first, 2 of 2)

- [Self-Adjusting Weighted Expected Improvement for Bayesian Optimization](https://mlsanity.com/paper/2306.04262/) (2023)
- [Hyperparameter Adaptive Search for Surrogate Optimization: A Self-Adjusting Approach](https://mlsanity.com/paper/2310.07970/) (2023)

## Abstract (arXiv)

Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problems. The BO pipeline is highly modular, with different design choices for the initial sampling strategy, the surrogate model, the acquisition function (AF), the solver used to optimize the AF, etc. We demonstrate in this work that a dynamic selection of the AF can benefit the BO design. More precisely, we show that already a na"ive random forest regression model, built on top of exploratory landscape analysis features that are computed from the initial design points, suffices to recommend AFs that outperform any static choice, when considering performance over the classic BBOB benchmark suite for derivative-free numerical optimization methods on the COCO platform. Our work hence paves a way towards AutoML-assisted, on-the-fly BO designs that adjust their behavior on a run-by-run basis.