Mike Preuss  •  Algorithm Engineering  •  TU Dortmund
	Research

momentary research objectives are:

Single-criterial global optimization via Evolutionary Algorithms:

EAs are generally not pure locally acting optimization algorithms. To a certain (controllable!) extend, they are also well suited for multimodal problems. Up to now, systematic investigation of this property has been largely neglected. I am interested in evaluating the possibilities and limits for EAs on these problem types. This includes approaches like Niching and Multistarts.

Experimental analysis of Evolutionary Algorithms:

Recent methods as the Sequential Parameter Optimization (SPO) by Thomas Bartz-Beielstein permit to locate good algorithm parameter sets for treating a concrete problem. Additionally, experimental data may be employed to obtain further insight: How do the parameters interact, and how difficult is it to adapt an optimization algorithm to a certain problem?

Computational Intelligence in Games:

Here, I am mostly interested in the design of new user-centric extensions of realtime strategy games and the construction/evolution of believable non-player characters (NPCs) to enhance player satisfaction. Moreover, the setting is a very difficult test environment for optimization/learning algorithms: High system dynamics, mutiple criteria, hard time constraints....

Multicriterial Evolutionary Algorithms:

Here I am most interested in the interrelation of Pareto set and Pareto front. Motivated by the multimodal viewpoint onto singleobjective problems we find interesting similarities: There is no reason to believe that the single target functions of multicriterial problems are always unimodal. However, this is an implicit assumption of most current multicriterial Evolutionary Algorithms.

Real-World applications of Evolutionary Algorithms:

Preferably, I am dealing with applications of EA on problems from the following domains: scheduling, classification, map labeling, thermodynamical process engineering, and elevator control.