Introduction to Computational Intelligence
Prof. Dr. Oliver Wendt
Dr. habil. Mahdi Moeini
Summer term 2020
Examination: The exam is in English and the assignments might be answered either in English or in German.
Due to the current situation, the lectures will be via recorded videos. Any further new information will be published on the OLAT page in which you can register. The OLAT page is protected by a password that you can ask it from Dr. habil. Mahdi Moeini: mahdi.moeini(at)wiwi.uni-kl.de
To avoid spams and stealthy registrations, only registrations with a valid RHRK e-mail address are permitted. Any registration by home-based e-mail addresses will be removed from the OLAT page without any further warning. Once you are on the OLAT page, you should register (check the left menu of the page) to be enrolled in the corresponding course(s) (CI or OLS or both).
The language of the lectures is English.
If you need further information, please contact Dr. habil. Mahdi Moeini via: mahdi.moeini(at)wiwi.uni-kl.de
The module "Computational Intelligence" comprises the two parts "Optimization of Logistics Systems" and "Introduction to Computational Intelligence". Both lectures are given in English. The whole module has 6 (2x3) ECTS.
Part: Introduction to 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.
Furthermore, the course has a section on programming with Python and an initiation to the commercial solver Gurobi. The course offers several practical programming sessions.