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040676 PR KFK PM/SCM/TL: Practical Course Metaheuristics I (2014W)
Continuous assessment of course work
Labels
Registration/Deregistration
Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
- Registration is open from Mo 15.09.2014 09:00 to We 24.09.2014 14:00
- Deregistration possible until Tu 14.10.2014 23:59
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
- Thursday 02.10. 08:00 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 09.10. 08:00 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 16.10. 08:00 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 23.10. 08:00 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 30.10. 08:00 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 06.11. 08:00 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 13.11. 08:00 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
- Thursday 20.11. 08:00 - 14:45 PC-Seminarraum 2 Oskar-Morgenstern-Platz 1 1.Untergeschoß
Information
Aims, contents and method of the course
Despite the recent advances in mathematical programming-based methods and solvers, approximate approaches (heuristics and metaheuristics) are still the optimization-based technology that is most widely used to support decision making in practice. The objective of this course is to provide students with the fundamental tools for designing, tuning, and testing heuristics and metaheuristics for hard combinatorial optimization problems. Besides that, we will also cover the fundamental concepts of complexity theory that are the key to understand the need for approximate approaches and to design efficient heuristics and metaheuristics.
Assessment and permitted materials
Short tests during the course: 60% (5x12% or 4x15%)
Project work: 40%
Project work: 40%
Minimum requirements and assessment criteria
At the end of this course, students will know what are metaheuristics, why they are needed, how to design them, and how to evaluate their quality. No implementation is required during this course, but it will be required during the follow-up course Metaheuristics II.
Examination topics
Reading list
[1] Handbook of Metaheuristics 2nd edition. Gendreau, M. & Potvin, J.-Y. (Eds.).Springer, ISBN 978-1-4419-1663-1
[2] Stochastic Local Search, Foundations and Applications. Hoos, H. & Stützle, T. Elsevier, ISBN 1-55860-872-9
[3] Search Methodologies, Introductory tutorials in optimization and decision support techniques. Burke, E. K. & Kendall, G. Springer, ISBN 0-387-23460-8
[2] Stochastic Local Search, Foundations and Applications. Hoos, H. & Stützle, T. Elsevier, ISBN 1-55860-872-9
[3] Search Methodologies, Introductory tutorials in optimization and decision support techniques. Burke, E. K. & Kendall, G. Springer, ISBN 0-387-23460-8
Association in the course directory
Last modified: Mo 07.09.2020 15:29