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

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.

preprint2022arXivOpen access
David H. BrookesAkosua BusiaClara FannjiangKevin MurphyJennifer Listgarten
Machine Learning
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Open access5 authors1 topic
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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David H. BrookesAkosua BusiaClara FannjiangKevin MurphyJennifer Listgarten

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