# Parallel black-box optimization of expensive high-dimensional multimodal functions via magnitude

arXiv: https://arxiv.org/abs/2201.11677 · HTML: https://mlsanity.com/paper/2201.11677/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2022.

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

- [Metric Space Magnitude for Evaluating the Diversity of Latent Representations](https://mlsanity.com/paper/2311.16054/) (2023)
- [Quality-diversity in dissimilarity spaces](https://mlsanity.com/paper/2211.12337/) (2022)

## Abstract (arXiv)

Building on the recently developed theory of magnitude, we introduce the optimization algorithm EXPLO2 and carefully benchmark it. EXPLO2 advances the state of the art for optimizing high-dimensional (D gtrapprox 40) multimodal functions that are expensive to compute and for which derivatives are not available, such as arise in hyperparameter optimization or via simulations.