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Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 24271)

Authors Anne Auger, Tobias Glasmachers, Martin S. Krejca, Johannes Lengler, Alexander Jungeilges and all authors of the abstracts in this report

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Author Details

Anne Auger
  • Inria Saclay Ile-de-France, Palaiseau, FR
Tobias Glasmachers
  • Ruhr University Bochum, DE
Martin S. Krejca
  • Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau, FR
Johannes Lengler
  • ETH Zürich, CH
Alexander Jungeilges
  • Ruhr University Bochum, DE
and all authors of the abstracts in this report

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Anne Auger, Tobias Glasmachers, Martin S. Krejca, Johannes Lengler, and Alexander Jungeilges. Theory of Randomized Optimization Heuristics (Dagstuhl Seminar 24271). In Dagstuhl Reports, Volume 14, Issue 6, pp. 215-244, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/DagRep.14.6.215

Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 24271 "Theory of Randomized Optimization Heuristics", which marks the twelfth installment of our biennial seminar series. This iteration saw a lot of discussion on important, yet rarely analyzed topics in the domain of heuristic optimization, such as mixed-integer problems, permutation spaces, and coevolution. Moreover, it aimed at unifying existing results by discussing mathematical tools (such as drift analysis), the structure of discrete problems, and a common framework for theoretical analysis and practical implementation. Last, more recent and important topics, such as constrained and multi-objective optimization, were a major part of the seminar. We had a vivid exchange in various breakout sessions and different talks, with a great mix of junior and senior participants, which was very positively received.

Subject Classification

ACM Subject Classification
  • Theory of computation → Evolutionary algorithms
  • Theory of computation → Theory of randomized search heuristics
Keywords
  • Black-Box Optimization Heuristics
  • Evolution Strategies
  • Genetic and Evolutionary Algorithms
  • Runtime and Convergence Analysis
  • Stochastic Processes

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