Computational Intelligence


Prof. Dr. Oliver Wendt
Daniel Schermer


Computational Intelligence

For many assignment and permutation problems an exponential growth of the number of solutions prohibits the application of optimization algorithms known from Operations Research. Rather, literature and practitioners resort to the application of heuristics. Heuristics come with much lower computational effort but as a downside - cannot provide a guarantee for the optimality of the solutions found. First, the course focuses on local search heuristics inspired by analogies to nature (Genetic Algorithms and Simulated Annealing) and Tabu Search and compares their applicability for different classes of planning problems. Furthermore, most decision processes do not only confront us with a high number of alternatives but also with uncertainty. We will show how Machine Learning (esp. Reinforcement Learning) can address this uncertainty in complex decision processes, when an appropriate representation of the search space and the value functions can be found. Artificial Neural Networks are introduced (as another paradigm in analogy to nature) as a computational solution of this representational problem.

Summer Term 2023

If you want to participate in Computational Intelligence then please make sure that the following conditions are met.

  1. You have registered in KIS.
  2. You have logged in to Olat at least once, using your university account (i.e., through Shibboleth).
  3. The profile of your Olat university account must still be associated with your (or email address.

If these conditions are met, you will be added directly to the corresponding Olat course during the first week of the lecture period.