A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization
Machine Learning
2022-06-14 v9 Machine Learning
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.
Keywords
Cite
@article{arxiv.1905.10474,
title = {A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization},
author = {David H. Brookes and Akosua Busia and Clara Fannjiang and Kevin Murphy and Jennifer Listgarten},
journal= {arXiv preprint arXiv:1905.10474},
year = {2022}
}