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

## TL;DR

This paper demonstrates that many Estimation of Distribution Algorithms can be interpreted as Monte Carlo Expectation-Maximization algorithms, providing a rigorous statistical framework for understanding and analyzing EDAs.

## Contribution

It establishes a novel connection between EDAs and EM algorithms, offering a new theoretical perspective on their operation and analysis.

## Key findings

- EDAs can be formulated as Monte Carlo EM algorithms
- Exact EM limit corresponds to infinite samples in EDAs
- Provides a rigorous statistical foundation for EDAs

## Abstract

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.