DagRep.12.2.87.pdf
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Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081)
Authors and all authors of the abstracts in this report
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Dagstuhl Reports, Volume 12, Issue 2
Volume: Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2022-08-23
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Anne Auger, Carlos M. Fonseca, Tobias Friedrich, Johannes Lengler, and Armand Gissler. Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 22081). In Dagstuhl Reports, Volume 12, Issue 2, pp. 87-102, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/DagRep.12.2.87
Abstract
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.
Subject Classification
ACM Subject Classification
- Theory of computation → Bio-inspired optimization
- Theory of computation → Evolutionary algorithms
- Theory of computation → Theory of randomized search heuristics
Keywords
- black-box optimization
- derivative-free optimization
- evolutionary and genetic algorithms
- randomized search algorithms
- stochastic gradient descent
- theoretical computer science
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