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Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081) Auger, Anne Fonseca, Carlos M. Friedrich, Tobias Lengler, Johannes Gissler, Armand black-box optimization derivative-free optimization evolutionary and genetic algorithms randomized search algorithms stochastic gradient descent theoretical computer science This report documents the program and the outcomes of Dagstuhl Seminar 22081 "Theory of Randomized Optimization Heuristics". This seminar is part of a biennial seminar series. This year, we focused on connections between classical topics of the community, such as Evolutionary Algorithms and Strategies (EA, ES), Estimation-of-Distribution Algorithms (EDA) and Evolutionary Multi-Objective Optimization (EMO), and related fields like Stochastic Gradient Descent (SGD) and Bayesian Optimization (BO). The mixture proved to be extremely successful. Already the first talk turned into a two hour long, vivid and productive plenary discussion. The seminar was smaller than previous versions (due to corona regulations), but its intensity more than made up for the smaller size. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Anne Auger and Carlos M. Fonseca and Tobias Friedrich and Johannes Lengler and Armand Gissler 2022 Is Part Of Dagstuhl Reports, Volume 12, Issue 2 (2022) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.12.2.87 urn:nbn:de:0030-drops-169325 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.12.2.87 eng https://creativecommons.org/licenses/by/4.0/legalcode