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arXiv · 1905.10474

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

David H. Brookes·Akosua Busia·Clara Fannjiang·Kevin Murphy·Jennifer Listgarten
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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.

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David H. Brookes, Akosua Busia, Clara Fannjiang, Kevin Murphy, Jennifer Listgarten. 2019-05-24. A view of Estimation of Distribution Algorithms through the lens of Expectation-Maximization. https://arxiv.org/abs/1905.10474

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