Business Information Systems & Operations Research

Introduction to Computational Intelligence

Prof. Dr. Oliver Wendt

Dr. habil. Mahdi Moeini

Summer term 2019

WIW-WIN-CIN-M-7

Examination: The exam is in English and the assignments might be answered either in English or in German.


Important notice:

The lectures take place as a block during June 24-27, 2019 in 42-421a.

The language of the lectures is English.

Here is the schedule of the lectures:

(*) Monday 24.06.2019: 9:00-12:00 and 14:00-17:00
(*) Tuesday 25.06.2019: 9:00-12:00 and 14:00-17:00
(*) Wednesday 26.06.2019: 9:00-12:00 and 14:00-17:00
(*) Thursday 27.06.2019: 9:00-12:00 and 14:00-17:00

Any fluctuation in the schedule will be announced on this page.

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:

Short Decsription:

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.

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