07.05.17 - 12.05.17, Seminar 17191

Theory of Randomized Optimization Heuristics

The following text appeared on our web pages prior to the seminar, and was included as part of the invitation.


Randomized search and optimization heuristics such as evolutionary algorithms, ant colony optimization, particle swarm optimization, and simulated annealing, have become established problem solvers. They have successfully been applied to a wide range of real-world applications, and they are applicable to problems that are non-continuous, multi-modal, and/or noisy as well as to multi-objective and dynamic optimization tasks.

The goal of the seminar is to advance the theory underlying randomized search and optimization heuristics in order to gain a better understanding of the algorithms and to develop new and more powerful methods in a principled way. The seminar will cover all important streams of research in the theory of randomized search and optimization heuristics with a focus on selected emergent topics, such as dynamic optimization problems and precise runtime bounds.

Our seminar invites researchers from the machine learning community working on Bayesian optimization and Monte Carlo methods for optimization. We are looking forward to discussing the similarities and differences of the approaches and to bridge the gap between the different communities involved.

The seminar continues the successful series of “Theory of Evolutionary Algorithms” Dagstuhl seminars, where the change in title reflects the development of the research field toward a broader range of heuristics.

Creative Commons BY 3.0 Unported license
Carola Doerr, Christian Igel, Lothar Thiele, and Xin Yao