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Instructors: Prof. Dr. Paul Kaufmann
Event type:
Lecture/practice class
Displayed in timetable as:
03.996.3310
Hours per week:
4
Language of instruction:
Englisch
Min. | Max. participants:
- | -
Contents:
Evolutionary Algorithms, Ant Colony Optimization, and Particle Swarm Optimization are nature-inspired algorithms in the field of 'Computational Intelligence' that can be used when an optimization problem becomes to large and to complex to be solved by a conventional approach efficiently. As our world turns digital, methods of Computational Intelligence become more and more popular for solving challenging tasks such as process optimization, market analysis, strategic planning, and logistics.
This lecture introduces modeling of technical and operational systems before proceeding with various algorithms from the area of Computational Intelligence. The lecture covers also multi-objective optimization and statistical methods for algorithm evaluation. The topics presented in the lecture will be subject to 'pencil and paper' as well as 'hands-on programming' exercises in the labs.
Recommended reading list:
- Weicker: Evolutionäre Algorithmen, Springer, 2007.
- Kruse et al.: Computational Intelligence - Eine methodische Einführung in Künstliche Neuronale Netze, Evolutionäre Algorithmen, Fuzzy-Systeme und Bayes-Netze. Springer, 2015.
- Kruse et al.: Computational Intelligence - A Methodological Introduction. Springer, 2013.
- Eiben and Smith: Introduction to evolutionary computing. Springer, 2015.
- Rothlauf: Design of Modern Heuristics - Principles and Application. Springer. 2011.
- Michalewicz et al.: How to Solve It: Modern Heuristics. Springer. 2004.
Additional information:
- Teaching language: English
- Examination language: English or German
- Examination type: Oral (30 min) or written (60 min)
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