Theory of Evolutionary Algorithms

Evolutionary algorithms, such as evolutionary programming (EP), evolution strategies (ES), genetic algorithms (GA), and genetic programming (GP), have attracted considerable interest as optimization heuristics during the past 10-15 years. Because of the exponential increase in computer power during the last decade, they are able to deal with real-world problems and new application domains are still arising.

Although evolutionary algorithms are easy to implement, the underlying process is complicated and stochastic, depending on the fitness function and the free parameters controlling variation and selection. The analysis of these stochastic processes seems to be much more difficult than the analysis of randomized algorithms for special purposes, e.g., primality testing.

The goal of our seminar is to contribute to the theory of evolutionary algorithms. As the second seminar on this topic it continues the "tradition" started with Dagstuhl Seminar 00071 in 2000: all aspects of analysis are considered, including but not limited to models of evolution as stochastic processes, dynamic and static performance investigations, progress rate analysis, and time complexity analysis.

The Seminar has the aim of bringing together researchers from all branches of evolutionary algorithms. Different points of view and different tools will be exchanged and we are hoping for synergistic effects of these discussions. For this reason not only computer scientists but also researchers from mathematics, physics, chemistry, and biology are invited.

Besides lectures on new results obtained by the participants we will have three keynote speakers presenting their different viewpoints on evolutionary algorithm theory. We plan to have an open problem session and plenty of time for informal discussions. This workshop style will be supported by the excellent facilities of Schloss Dagstuhl.