February 20 – 25 , 2022, Dagstuhl Seminar 22081

Theory of Randomized Optimization Heuristics


Anne Auger (INRIA Saclay – Palaiseau, FR)
Carlos M. Fonseca (University of Coimbra, PT)
Tobias Friedrich (Hasso-Plattner-Institut, Universität Potsdam, DE)
Johannes Lengler (ETH Zürich, CH)

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Dagstuhl Report, Volume 12, Issue 2 Dagstuhl Report
Aims & Scope
List of Participants
Shared Documents
Dagstuhl's Impact: Documents available
Dagstuhl Seminar Schedule [pdf]


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 Evolutionary 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 hours long, vivid and productive plenary discussion. Participants like Peter Richtarik and Sebastian Stich brought valuable new perspectives from the SGD community, and Mickaël Binois contributed the BO perspective. This yielded some new approaches to long-standing open problems, specifically for a convergence proof of the CMA-ES algorithm on quadratic functions.

Another interesting and fruitful aspect of the seminar was a shift of perspective to search spaces that are under-represented in the community. Traditionally, the search spaces are product spaces, either discrete (especially the n-dimensional hypercube), or continuous (d-dimensional Euclidean space). This year we had some intense discussions in plenum and in working groups on other search spaces, triggered especially by Ekhine Irurozki's presentation on permutation spaces.

Naturally, a big part of the seminar was also devoted to classical topics of the community. Highlights included talks by Benjamin Doerr on the first runtime result for the Non-Dominated Sorting Genetic Algorithm (NSGA-II) and by Tobias Glasmachers on Convergence Analysis of the Hessian Estimation Evolution Strategy (HE-ES). The latter is the first convergence proof for a covariance matrix algorithm that does not truncate the condition number of the estimated covariance matrix. Some interesting new topics were also identified in traditional fields, such as whether we can understand better in which situations adapativity is necessary for efficient optimization by considering k-adaptive query complexity of optimization benchmarks.

Overall, as organizers we were extremely happy with the mix of core community members and researchers from related fields. The connections with the latter were close enough that scientific discussions could (also) happen on technical levels, which is particularly useful since some low-hanging fruits are available from such interchanges. Importantly, the exchange happened between people who would probably not have met each other outside of the Dagstuhl Seminar.

The seminar took place during the peak of the Omicron wave of Covid19, which made planning very difficult. The key step during preparation phase was a survey among the participants a few weeks before the seminar. We asked how likely it was that they could participate in person, and under which circumstances they would prefer which format (in-person or hybrid). The participants signalled us very clearly that they wanted this event to happen, and that they wanted it to happen in person. We want to thank all participants for their support! Other seminars in the week before and after ours had to be cancelled altogether, and this might also have happened to our seminar if not for the determination of our participants.

The seminar was smaller than previous versions, due to corona regulations. Moreover, some participants had to cancel at the last moment because they were corona-positive, or because they had no reliable child care. Especially the latter point can be frustrating, and we hope that Dagstuhl will be able to resume their support for on-site child care in the future. On the positive side, the intensity of the seminar more than made up for the smaller size, and might even have been due to the smaller number of participants.

Finally, we want to thank Dagstuhl for their great support, both financially and to their great staff. We could always feel that it was their top priority to help us, and we are greatly indebted for the support!

The organizers,
Anne Auger, Carlos M Fonseca, Tobias Friedrich, Johannes Lengler

Summary text license
  Creative Commons BY 4.0
  Anne Auger, Carlos M. Fonseca, Tobias Friedrich, and Johannes Lengler

Dagstuhl Seminar Series


  • Neural And Evolutionary Computing


  • Black-box optimization
  • Evolutionary and genetic algorithms
  • Stochastic gradient descent
  • Randomized search algorithms
  • Theoretical computer science
  • Derivative-free optimization


In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.


Download overview leaflet (PDF).

Dagstuhl's Impact

Please inform us when a publication was published as a result from your seminar. These publications are listed in the category Dagstuhl's Impact and are presented on a special shelf on the ground floor of the library.


Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.