Estimation-of-distribution algorithms (EDAs) are a relatively recent type of randomized optimization heuristics that iteratively develop a probabilistic model of good solutions in the underlying search space. They thus differ from classical randomized heuristics such as local search, simulated annealing, or genetic algorithms in that they are not restricted to sets of search points as the only mean of carrying information from one iteration to the next. EDAs are successfully applied in various engineering areas. In the previous 7–8 years, they received increasing attention also in theoretical research, pointing out critical influences of their main parameters and rigorously demonstrating situations in which EDAs are superior to many classical approaches, among others, in leaving local optima and in dealing with noise. So far almost all theoretical efforts in EDAs have focused on understanding univariate probabilistic models. The benefits of EDAs, however, are likely to stand out even more if one considers multivariate EDAs, which empirically have been shown to outperform classical evolutionary algorithms on several classes of problems where learning dependencies among decision variables reveals itself to be crucial.

The purpose of this Dagstuhl Seminar is to bring together researchers from the theory and the applications of EDAs. In a small number of survey talks, they will summarize the state of the art in the sub-disciplines with significant recent progress. There will also be a small number of talks discussing in depth recent breakthrough results. A large proportion of the time will be devoted to discussions, both plenary and in small groups. In these, we shall try to clarify how the recent theoretical findings can be used to make EDAs more successful in practice, what experience in practice would be worth making rigorous via theoretical works, and what are the most interesting directions for future research, ideally via combined theoretical and applied approaches.

Creative Commons BY 4.0

Josu Ceberio Uribe, Benjamin Doerr, John McCall, and Carsten Witt

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• Alistair Benford (University of Birmingham, GB) [dblp]
• Jasmin Brandt (Universität Paderborn, DE)
• Josu Ceberio Uribe (University of the Basque Country - Donostia, ES) [dblp]
• Duc-Cuong Dang (Universität Passau, DE) [dblp]
• Benjamin Doerr (Ecole Polytechnique - Palaiseau, FR) [dblp]
• Carola Doerr (Sorbonne University - Paris, FR) [dblp]
• Martin Fyvie (The Robert Gordon University - Aberdeen, GB) [dblp]
• Nikolaus Hansen (INRIA Saclay - Palaiseau, FR) [dblp]
• Jose Ignacio Hidalgo (Complutense University of Madrid, ES) [dblp]
• Joshua D. Knowles (SLB Cambridge Research, GB) [dblp]
• Timo Kötzing (Hasso-Plattner-Institut, Universität Potsdam, DE) [dblp]
• Martin S. Krejca (Ecole Polytechnique - Palaiseau, FR) [dblp]
• Johannes Lengler (ETH Zürich, CH) [dblp]
• John McCall (The Robert Gordon University - Aberdeen, GB) [dblp]
• Aishwarya Radhakrishnan (Hasso-Plattner-Institut, Universität Potsdam, DE) [dblp]
• Franz Rothlauf (Universität Mainz, DE) [dblp]
• Roberto Santana (University of the Basque Country - Donostia, ES) [dblp]
• Valentino Santucci (University for Foreigners of Perugia, IT) [dblp]
• Marcus Schmidbauer (Universität Passau, DE)
• Jonathan L. Shapiro (University of Manchester, GB) [dblp]
• Marta Rosa Soto Ortiz (CaixaBank Tech - Madrid, ES)
• Andrew M. Sutton (University of Minnesota - Duluth, US) [dblp]
• Dirk Thierens (Utrecht University, NL) [dblp]
• Vanessa Volz (CWI - Amsterdam, NL) [dblp]
• Carsten Witt (Technical University of Denmark - Lyngby, DK) [dblp]