040676 KU Metaheuristics (MA) (2020W)
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 14.09.2020 09:00 to We 23.09.2020 12:00
- Registration is open from Mo 28.09.2020 09:00 to We 30.09.2020 12:00
- Deregistration possible until Sa 31.10.2020 12:00
Details
max. 30 participants
Language: English
Lecturers
Classes (iCal) - next class is marked with N
Wednesday
07.10.
11:30 - 13:00
Digital
Wednesday
14.10.
11:30 - 13:00
Digital
Wednesday
21.10.
11:30 - 13:00
Digital
Wednesday
28.10.
11:30 - 13:00
Digital
Wednesday
04.11.
11:30 - 13:00
Digital
Wednesday
11.11.
11:30 - 13:00
Digital
Wednesday
18.11.
11:30 - 13:00
Digital
Wednesday
25.11.
11:30 - 13:00
Digital
Wednesday
02.12.
11:30 - 13:00
Digital
Wednesday
09.12.
11:30 - 13:00
Digital
Wednesday
16.12.
11:30 - 13:00
Digital
Wednesday
13.01.
11:30 - 13:00
Digital
Wednesday
20.01.
11:30 - 13:00
Digital
Wednesday
27.01.
11:30 - 13:00
Digital
Information
Aims, contents and method of the course
Assessment and permitted materials
* [40%] Four short exams á ca. 15 minutes (written, 10% each)
* [45%] Project work (choose one):
- Programming a metaheuristic for an optimisation problem
- Read and study (i.e., summarise, analyse and criticise) a scientific paper
* [15%] Oral presentation of project
* [45%] Project work (choose one):
- Programming a metaheuristic for an optimisation problem
- Read and study (i.e., summarise, analyse and criticise) a scientific paper
* [15%] Oral presentation of project
Minimum requirements and assessment criteria
In order to obtain a positive grade on the course, at least 50% of the overall points have to be achieved. The grades are distributed as follows:
1: 87% to 100%
2: 75% to <87%
3: 63% to <75%
4: 50% to <63%
5: <50%
1: 87% to 100%
2: 75% to <87%
3: 63% to <75%
4: 50% to <63%
5: <50%
Examination topics
* 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
Reading list
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.
Association in the course directory
Last modified: 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