# The True Destination of EGO is Multi-local Optimization

arXiv: https://arxiv.org/abs/1704.05724 · HTML: https://mlsanity.com/paper/1704.05724/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2017.

## Built on by (most cited first, 5 of 5)

- [Discovering Many Diverse Solutions with Bayesian Optimization](https://mlsanity.com/paper/2210.10953/) (2022)
- [Behavior-based Neuroevolutionary Training in Reinforcement Learning](https://mlsanity.com/paper/2105.07960/) (2021)
- [Full Domain Analysis in Fluid Dynamics](https://mlsanity.com/paper/2505.22275/) (2025)
- [Experimental Investigation and Evaluation of Model-based Hyperparameter Optimization](https://mlsanity.com/paper/2107.08761/) (2021)
- [Beyond Regrets: Geometric Metrics for Bayesian Optimization](https://mlsanity.com/paper/2401.01981/) (2024)

## Abstract (arXiv)

Efficient global optimization is a popular algorithm for the optimization of expensive multimodal black-box functions. One important reason for its popularity is its theoretical foundation of global convergence. However, as the budgets in expensive optimization are very small, the asymptotic properties only play a minor role and the algorithm sometimes comes off badly in experimental comparisons. Many alternative variants have therefore been proposed over the years. In this work, we show experimentally that the algorithm instead has its strength in a setting where multiple optima are to be identified.