About the course: The course will introduce some ideas and algorithms from the field of multiple-objective optimization. Each week, we will jump into very different topics, such as classical methods, evolutionary algorithms, and more.
Prerequisites:
- Python 3.8
- Jupyter notebook; required libraries: matplotlib, numpy
- It is suggested to use own laptops
- Your scripts will be evaluated during the laboratories. However, you are also asked to send them via e-mail. Please, begin e-mail titles with “[MOO].”
Grading (labs): link
Assign to the course here: link to google spreadsheet
Important note: Your solutions will be checked during the laboratories. However, you are also asked to send your codes one day before the laboratory meeting (just Python + linked files, i.e., do not send IDE projects, etc.). The reason is that your solutions will be occasionally checked for plagiarism. Some requirements:
- When sending an email, do not forget to put the “[MOO]” prefix in the title.
- Remember to include your names, student IDs, and task no. in the message.
- Send your scripts packed in a zip file named “ID1_ID2.zip” where ID1 and ID2 are your student IDs (or name it “ID1.zip”, i.e., put your student ID, if you are not working in a pair).
Calendar: see the course on the eKursy system
Topics (materials can be found at eKursy):
| NO | TOPIC |
| 1 | Introduction + Example problems + Classical methods |
| 2 | Evolutionary Multiple-objective optimization (Part 1) |
| 3 | Evolutionary Multiple-objective optimization (Part 2) |
| 4 | Quality assessment & visualization |
| 5 | Challenges in multiple-objective optimization |
| 6 | Preference-based methods |