040676 KU Metaheuristics (MA) (2021S)
Prüfungsimmanente Lehrveranstaltung
Labels
DIGITAL
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
- Anmeldung von Do 11.02.2021 09:00 bis Mo 22.02.2021 12:00
- Anmeldung von Do 25.02.2021 09:00 bis Fr 26.02.2021 12:00
- Abmeldung bis Mi 31.03.2021 23:59
Details
max. 30 Teilnehmer*innen
Sprache: Englisch
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
Mittwoch
03.03.
09:45 - 11:15
Digital
Mittwoch
10.03.
09:45 - 11:15
Digital
Mittwoch
17.03.
09:45 - 11:15
Digital
Mittwoch
24.03.
09:45 - 11:15
Digital
Mittwoch
14.04.
09:45 - 11:15
Digital
Mittwoch
21.04.
09:45 - 11:15
Digital
Mittwoch
28.04.
09:45 - 11:15
Digital
Mittwoch
05.05.
09:45 - 11:15
Digital
Mittwoch
12.05.
09:45 - 11:15
Digital
Mittwoch
19.05.
09:45 - 11:15
Digital
Mittwoch
26.05.
09:45 - 11:15
Digital
Mittwoch
02.06.
09:45 - 11:15
Digital
Mittwoch
09.06.
09:45 - 11:15
Digital
Mittwoch
16.06.
09:45 - 11:15
Digital
Mittwoch
23.06.
09:45 - 11:15
Digital
Mittwoch
30.06.
09:45 - 11:15
Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
* Four short written tests during the course, no material allowed: 40% (4x10%)
* Project work (choose one): 45%
- programming a metaheuristic for an optimisation problem
- read and study a scientific paper
* Oral presentation of the project: 15%
* Project work (choose one): 45%
- programming a metaheuristic for an optimisation problem
- read and study a scientific paper
* Oral presentation of the project: 15%
Mindestanforderungen und Beurteilungsmaßstab
Appropriate points will be assigned to each part of the exam and to the possible homework, the grading will be scaled in 100%.
In order to pass the course (minimum requirement) students have to achieve at least 50% in total.The other grades are distributed as follows:
4: 50% to <63%
3: 63% to <75%
2: 75% to <87%
1: 87% to 100%
In order to pass the course (minimum requirement) students have to achieve at least 50% in total.The other grades are distributed as follows:
4: 50% to <63%
3: 63% to <75%
2: 75% to <87%
1: 87% to 100%
Prüfungsstoff
* Analysis of algorithms and complexity theory (basics)
* Local search methods
* Nature-inspired metaheuristics
* Construction-based metaheuristics
* Local search methods
* Nature-inspired metaheuristics
* Construction-based metaheuristics
Literatur
The teaching material (slides, sample code, further reading, etc.) is available on the e-learning platform Moodle.Useful literature:
1. M. Gendreau and J.-Y. Potvin (2010), editors, Handbook of Metaheuristics, 2nd edition, Springer, 648 pages.
2. E. K. Burke and G. Kendall (2014), editors, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 2nd edition, Springer, 716 pages.
3. H. H. Hoos and T. Stützle (2005), Stochastic Local Search: Foundations and Applications, Elsevier, 658 pages.
1. M. Gendreau and J.-Y. Potvin (2010), editors, Handbook of Metaheuristics, 2nd edition, Springer, 648 pages.
2. E. K. Burke and G. Kendall (2014), editors, Search Methodologies: Introductory Tutorials in Optimization and Decision Support Techniques, 2nd edition, Springer, 716 pages.
3. H. H. Hoos and T. Stützle (2005), Stochastic Local Search: Foundations and Applications, Elsevier, 658 pages.
Zuordnung im Vorlesungsverzeichnis
Letzte Änderung: Fr 12.05.2023 00:13
Metaheuristics are particularly attractive in the efficient and effective solution of logistic decision problems in supply chains, transportation, telecommunications, vehicle routing and scheduling, manufacturing and machine scheduling, timetabling, sports scheduling, facility location and layout, and network design, among other areas.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 understanding the need for approximate approaches and to design efficient heuristics and metaheuristics.
1. A gentle introduction to the analysis of algorithms and complexity theory
2. Historical and modern local search methods
3. Nature-inspired metaheuristics
4. Construction-based metaheuristics