A view of estimation of distribution algorithms through the lens of expectation-maximization
David H. Brookes, Akosua Busia, Clara Fannjiang, Kevin J. Murphy, Jennifer Listgarten · 2020
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.