 | Study programme 2019-2020 | Français | |
| Multi-Objective Optimization | |||
Learning Activity |
| Code | Lecturer(s) | Associate Lecturer(s) | Subsitute Lecturer(s) et other(s) | Establishment |
|---|---|---|---|---|
| I-MARO-231 |
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| Language of instruction | Language of assessment | HT(*) | HTPE(*) | HTPS(*) | HR(*) | HD(*) | Term |
|---|---|---|---|---|---|---|---|
| Anglais | Anglais | 3 | 9 | 0 | 0 | 0 | Q1 |
| Organisational online arrangements for the end of Q3 2019-2020 assessments (Covid-19) |
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| Description of the modifications to the Q3 2019-2020 online assessment procedures (Covid-19) |
| The evaluation is based on the project report (written production remotely via Moodle examens) carried out by group of two students. |
Content of Learning Activity
Organization of the class: Introduction and background on Multi-Objective Optimization (search space, objective space, Pareto optimality, Pareto front,...). Several basic and advanced optimization methods are presented to solve Multi-Objective Optimization problems. Some tools are presented to evaluate the performance of Multi-Objective algorithms.
Required Learning Resources/Tools
Not applicable
Recommended Learning Resources/Tools
Not applicable
Other Recommended Reading
Not applicable
Mode of delivery
- Face to face
Type of Teaching Activity/Activities
- Cours magistraux
- Travaux pratiques
Evaluations
The assessment methods of the Learning Activity (AA) are specified in the course description of the corresponding Educational Component (UE)
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term