# A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization

arXiv: https://arxiv.org/abs/1905.10474 · HTML: https://mlsanity.com/paper/1905.10474/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 May 2019.

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

- [On the Importance of Environments in Human-Robot Coordination](https://mlsanity.com/paper/2106.10853/) (2021)
- [Variational Search Distributions](https://mlsanity.com/paper/2409.06142/) (2024)
- [Is novelty predictable?](https://mlsanity.com/paper/2306.00872/) (2023)
- [Natural Evolution Strategies as a Black Box Estimator for Stochastic Variational Inference](https://mlsanity.com/paper/2308.08053/) (2023)
- [Leveraging Discrete Function Decomposability for Scientific Design](https://mlsanity.com/paper/2511.03032/) (2025)

## Abstract (arXiv)

We show that a large class of Estimation of Distribution Algorithms, including, but not limited to, Covariance Matrix Adaption, can be written as a Monte Carlo Expectation-Maximization algorithm, and as exact EM in the limit of infinite samples. Because EM sits on a rigorous statistical foundation and has been thoroughly analyzed, this connection provides a new coherent framework with which to reason about EDAs.