CHAIR OF

COMPUTATIONAL

INTELLIGENCE

The review of the EMO exam takes place on October 6th. You need to register to attend the review. Further information can be found here.

--- Announcements: EMO exam on 13th July ---

The entrance to exam starts at 7 am on Monday 13 July. Please be on time. Thanks.

The EMO final exam will take place on 13th July from 8:00 to 10:00 in Hörsaal 1 (G26-H1). For the exam, you are only allowed to take one DIN A4 sheet with annotations with you (either printed or hand-written), which we will take at the end along with the exam. No calculators or additional tools are required. Please bring your student id card and be there on time (exam takes place exactly at 8:00, and entering the room may take some additional time than usual). For the new hygienic rules regarding Covid19 situation, you should receive an email from the examination office. If not, please contact with them to be aware of the new rules.

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NEW: We have organised a Q/A session for Thursday 9th July (5 pm - 6 pm CEST). Zoom information in moodle (elearning platform)

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Description 

In our daily lives we are inevitably involved in optimization. How to get to the university in the least time is a simple optimization problem that we encounter every morning. Just looking around ourselves we can see many examples of optimization problems even with conflicting objectives and higher complexities. It is natural to want everything to be as good as possible, in other words optimal. The difficulty arises when there are conflicts between different goals and objectives. Indeed, there are many real-world optimization problems with multiple conflicting objectives in science and industry, which are of great complexity. We call them Multi-objective Optimization Problems.
Over the past decade, lots of new ideas have been investigated and studied to solve such optimization problems as any new development in optimization which can lead to a better solution of a particular problem is of considerable value to science and industry. Among these methods, evolutionary algorithms are shown to be quite successful and have been applied to many applications.

This course addresses the basic and advanced topics in the area of evolutionary multi-objective optimization and contains the following content:

• Introduction to single-objective optimization (SO) and multiobjective optimization (MO), classical methods for solving MO, definitions of Pareto-optimality and other theoretical foundations for MO
• Basics of evolutionary algorithms (algorithms, operators, selection mechanisms, coding and representations)
• Evolutionary multi-objective algorithms (NSGA-II, EMO scalarization methods such as MOEA/D)
• Large-scale EMO: large scale decision space and many objective optimization (such as NSGA-III)
• Constraint handling in SO and MO, robust optimization in EMO, surrogate methods for expensive function evaluations
• Evaluation mechanisms (Design of experiments, test problems, metrics, visualization)

Team

• Sanaz Mostaghim (Lectures)
• Cristian Ramirez Atencia (Tutorials)
• Julia Reuter
• Boris Djartov

Lectures 

The lectures will start in an online format (due to the current situation). We will provide you the recorded lectures which you can access from home at anytime.

In case that the situation changes, the lectures will take place Thursdays 13:00 -14:30 in G29-307. We will inform you, when we officially start with live lectures. 

Please look at the "Instructions for Summer Semester 2020" before you start: https://mediasite.ovgu.de/Mediasite/Play/8a6bface2d614942b977354d77bc60371d 

Please note that you need to use your URZ account to access the videos. 

You can access the viedo recordings of the lectures click here. 

Lecture Plan

In order to plan the lectures in a structured way, we provide you the following lecture plan. You should learn the material for the corresponding specified week by referring to the online videos and slides:

Slides

To open the slides, you need to use a password which is specified in the recorded lecture "Organization for SS2020" (access information above).

• Chapter 0: Organization 
• Chapter 1: Introduction 
• Chapter 2: Basic Principles of Multi-Objective Optimization 
• Chapter 3: Evolutionary Algorithms
• Chapter 4: Multi-Objective Evolutionary Algorithms
• Chapter 5: Robust Optimization and Constraint Handling
• Chapter 6: Evaluation mechanisms

Tutorials

For the lecture there will be weekly tutorials. In order to write the exam at the end of the lecture, you must attend and actively participate in one of the tutorial groups. New assignments will be published here every week. To attend, you must first apply for a spot in one of the groups (see below).

Participation in the tutorials will be over the streaming portal Zoom. You require a microphone for presenting your solution in the online lectures. You should install the Zoom client for being able to use the Annotation function. If you do not install it and use the browser instead, you would not be able to annotate during your presentation. A connection test will be held on the week 20th-24th April to assure that all students are able to connect to the streaming portal. First tutorial will be held the following week.

You must prepare answers to the written assignments at home and upload them by the given deadline on the Moodle webpage of the course. Each answer that you submit for an assignment means that you volunteer for that assignment. Take into account that these answers will be then used in your presentation.

At the beginning of each tutorial, we will ask you to present one of the assignments that you prepared. You pass the tutorial (and are allowed to write the exam) only if you volunteer for at least 2/3 of all assignments and presented a solution at least two times. 

In case that the situation changes, the tutorials will take place on the locations specified in the following table. We will inform you, when we officially start with live tutorials.

In order to attend a tutorial group, you will need to apply for a spot in one of the groups. This done via the LSF system from 2nd March to 15th April. Please use this link to see the tutorial information in the LSF system. After you log in with your student account, you can register for the tutorials and give preferences for each of the four groups (Please select all the group that fit with your schedule, and then give preferences for each of them). We will assign the free spots in the groups based on the preferences you gave for each group. Important: There is only limited space per tutorial group and we can only offer four weekly tutorial groups. Therefore, if there are too many applicants, it might be that not every applicant can attend this lecture and the tutorials. Note that the final assignment of the free spots will be done after the application deadline is over, and you will be informed via email if you can participate in this course or not.

Note: Each week, one assignent sheet will be discussed. You only volunteer and present for assignments in the respective week where these assignments are scheduled to be discussed. Further, you should only volunteer and present in your own tutorial group. Visiting the other tutorial groups is possible, but it should remain an exception (e.g. due to illness or other important appointments) and should be announced beforehand. You should notify your TA about your absence beforehand. You can then attend another tutorial group in that week. If you have a certificate of illness from your physician, the respective assignment sheet will not be counted when calculating your percentage of solved assignments in the end.

At some point, there would be some programming assignments, where the PlatEMO framework (in MATLAB) will be used. A PlatEMO Manual can be found in this link.

Assignments

• Assignments 1 (submit before 26.04.2020 23:59 CET)
• Assignments 2 (submit before 03.05.2020 23:59 CET)
• Assignments 3 (submit before 10.05.2020 23:59 CET)
• Assignments 4 (submit before 24.05.2020 23:59 CET)
• Assignments 5 (submit before 07.06.2020 23:59 CET)
• Assignments 6 (submit before 14.06.2020 23:59 CET)
• Assignments 7 (submit before 21.06.2020 23:59 CET)
• Assignments 8 (submit before 28.06.2020 23:59 CET) - Cone code template
• Assignments 9 (submit before 05.07.2020 23:59 CET)

Literature

•  Deb, Kalyanmoy. Multi-Objective Optimization Using Evolutionary Algorithms, Wiley, 2001.
• Coello, Carlos A. Coello, Gary B. Lamont, and David A. Van Veldhuizen. Evolutionary algorithms for solving multi-objective problems. Vol. 5. New York: Springer, 2007.
• Miettinen, Kaisa. Nonlinear multiobjective optimization. Vol. 12. Springer Science & Business Media, 2012.
• Ehrgott, Matthias. Multicriteria optimization. Vol. 491. Springer Science & Business Media, 2005.
• Kruse, Rudolf, et al. Computational intelligence: a methodological introduction. Springer, 2016.