CHAIR OF

COMPUTATIONAL

INTELLIGENCE

+++++ The Exam Review will take place on Thursday the 5th October at 11:00 c.t. in G29-018. Please bring your student ID.

Description

In our daily life, 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, many 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 multi-objective 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)
• 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)
• Julia Heise (Tutorial) 

Lectures 

Time: Tuesdays 11:00 – 12:30

Room: Auditorium, G29-307

Slides

• Chapter 1: Organization and 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
• Chapter 7: Advanced Topics in EMO (Many-Objective Optimization, Large-Scale Optimization and Parallel Optimization)

Recorded Lectures

The recorded lectures can be found on Mediasite: /OVGU/Fakultäten/Informatik (FIN)/Institut für Intelligente Kooperierende Systeme (IKS)/AG Computational Intelligence/Evolutionary Multi Objective Optimization

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

Auditorium Tutorials

Time: Fridays, 15:00 -17:00
Room: Auditorium, G29-307

For the lecture, there will be a weekly auditorium tutorial. The solutions for the tasks are presented in those tutorials. The participants are encouraged to ask any questions regarding the task during the tutorial or per mail beforehand.

The sheets will include theoretical and practical tasks, e.g. explaining and analysing certain concepts or applying methods and calculations. Programming assignments are also possible.

To pass the tutorials and get the exam approval, the participants will need to pass a Mid-Term Exam that will test their current understanding of the topics that were addressed so far. The date of the Mid-Term-Exam will be announced in the first week. It will take place during one of the Auditorium Tutorials.

The solutions of the tasks will not be uploaded. We encourage the students to exchange with each other and actively participate in the tutorials to optimize the learning experience.

Task Sheets (preliminary)

You can use the trainings exam to see how well-prepared you are. Note that this an old exam. The tasks and the content changed over time.

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.