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Theoretical Foundations of Evolutionary Multi-Objective Optimization

Tobias Friedrich
Professor and Chair of Theoretical Computer Science
University of Jena, Germany



Evolutionary algorithms have been shown to be especially successful when dealing with multi-objective problems. The area of evolutionary multi-objective optimization has grown rapidly during the last years. This tutorial will give an overview on the different results that have been achieved on the theoretical aspects of evolutionary multi-objective optimization. The two main parts of the talk will be the achieved approximation of the Pareto front and the hypervolume indicator.


Aims of EMO, Approximation of Pareto sets, Hypervolume Indicator, Approximation-Guided EA, Complexity of Hypervolume and Hypervolume Contribution, Relationship between Hypervolume and Approximation.

Expected enrollment:

Researchers interested in the theoretical foundations of evolutionary multi-objective optimization.



Tobias Friedrich received his MSc in computer science from the University of Sheffield, UK, in 2002 and his diploma in mathematics from the University of Jena, Germany, in 2005. In 2007 he obtained a PhD in theoretical computer science from Saarland University, Germany. Afterwards, he was a postdoc in the Algorithms Group of the International Computer Science Institute Berkeley, USA. From 2011 till 2012 he was senior researcher at the Max Planck Institute for Informatics and independent research group leader at the Cluster of Excellence on "Multimodal Computing and Interaction" in Saarbrücken. Since August 2012 he is full professor and chair of theoretical computer science at the University of Jena, Germany.


The length of the tutorial:
two hours.


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