17.05.15 - 22.05.15, Seminar 15211

Theory of Evolutionary Algorithms

Diese Seminarbeschreibung wurde vor dem Seminar auf unseren Webseiten veröffentlicht und bei der Einladung zum Seminar verwendet.

Motivaton

Evolutionary algorithms (EAs) are randomized search and optimization methods applicable to problems that are non-continuous, multi-modal, and/or noisy as well as to multi-objective and dynamic optimization tasks. They have successfully been applied to a wide range of real-world applications and demonstrated impressive performance in benchmarks for derivative-free optimization. The goal of this Dagstuhl Seminar is to advance the theory underlying evolutionary algorithms and related methods, in order to gain a better understanding of their properties and to develop new powerful methods in a principled way. The highly international, interdisciplinary seminar will bring together leading experts and young researchers in the field.

The seminar will cover all important streams of research in the theory of evolutionary algorithms with a focus on three topics of current interest:

Rigorous runtime and computational complexity analysis have become the most important tools in the theory of discrete evolutionary computation. The Dagstuhl Seminar series “Theory of Evolutionary Algorithms” has sparked this development, and the drastic increase in new results and young researchers entering this field naturally leads to this focus topic.

Using concepts from information geometry in evolutionary algorithms is one of the most promising new theoretical directions in evolutionary computing and will accordingly be further investigated at the seminar.

Evolutionary computing is rooted in theories of natural evolution, and many early approaches to understand basic properties of evolutionary algorithms were inspired by biological evolution theory. Still, today these two research fields are almost completely separated. We see a great potential for mutual benefit, which we would like to explore by inviting experts from evolution biology.