# Bayesian Optimization with Dimension Scheduling: Application to Biological Systems

arXiv: https://arxiv.org/abs/1511.05385 · HTML: https://mlsanity.com/paper/1511.05385/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2015.

## Built on by (most cited first, 9 of 9)

- [A survey and benchmark of high-dimensional Bayesian optimization of discrete sequences](https://mlsanity.com/paper/2406.04739/) (2024)
- [Computationally Efficient High-Dimensional Bayesian Optimization via Variable Selection](https://mlsanity.com/paper/2109.09264/) (2021)
- [High Dimensional Bayesian Optimization with Kernel Principal Component Analysis](https://mlsanity.com/paper/2204.13753/) (2022)
- [Anytime Model Selection in Linear Bandits](https://mlsanity.com/paper/2307.12897/) (2023)
- [ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein Evolution](https://mlsanity.com/paper/2205.09548/) (2022)
- [High-dimensional Bayesian Optimization with Group Testing](https://mlsanity.com/paper/2310.03515/) (2023)
- [Expected Coordinate Improvement for High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2404.11917/) (2024)
- [Leveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testing](https://mlsanity.com/paper/2504.06111/) (2025)
- [An Adaptive Dropout Approach for High-Dimensional Bayesian Optimization](https://mlsanity.com/paper/2504.11353/) (2025)

## Abstract (arXiv)

Bayesian Optimization (BO) is a data-efficient method for global black-box optimization of an expensive-to-evaluate fitness function. BO typically assumes that computation cost of BO is cheap, but experiments are time consuming or costly. In practice, this allows us to optimize ten or fewer critical parameters in up to 1,000 experiments. But experiments may be less expensive than BO methods assume: In some simulation models, we may be able to conduct multiple thousands of experiments in a few hours, and the computational burden of BO is no longer negligible compared to experimentation time. To address this challenge we introduce a new Dimension Scheduling Algorithm (DSA), which reduces the computational burden of BO for many experiments. The key idea is that DSA optimizes the fitness function only along a small set of dimensions at each iteration. This DSA strategy (1) reduces the necessary computation time, (2) finds good solutions faster than the traditional BO method, and (3) can be parallelized straightforwardly. We evaluate the DSA in the context of optimizing parameters of dynamic models of microalgae metabolism and show faster convergence than traditional BO.