Keynote speakers

To be completed

Keynote 1: Prof. Guenther Raidl (TUWien, Vienna, Austria)

Title: (Reinforcement) Learning for Guiding Metaheuristics

Abstract: 

The machine learning boom of the last years also led to interesting new developments in the area of metaheuristics.
Classical heuristic techniques for approaching hard combinatorial optimization problems are frequently based on construction heuristics, local search but also tree search, sometimes in combination with (mixed integer) linear programming or constraint programming principles.

While end-to-end machine learning approaches are still far from replacing these established techniques in combinatorial optimization, it has been recognized that the latter may benefit from incorporating learning for certain purposes.
One may say the aim is to ``learn how to better optimize''.

This talk will give an overview on some promising recent developments in this direction. For example for tree search based approaches, variants have been proposed that learn better performing branching and node selection strategies. In beam search, guidance heuristics may be learned that yield better results than leading manually crafted heuristics. Large neighborhood search approaches were proposed in which the construction of the neighborhoods to be applied is learned.
Some of these methods rely on imitation or supervised learning where labeled training data or some powerful other method to learn from need to be available. More versatile may be methods based on reinforcement learning principles, on which we will also have a look at.

Biography: Günther Raidl is Professor at the Institute of Logic and Computation, TU Wien, Austria, and member of the Algorithms and Complexity Group. He received his PhD in 1994 and completed his habilitation in Practical Computer Science in 2003 at TU Wien. In 2005 he received a professorship position for combinatorial optimization at TU Wien.

His research interests include algorithms and data structures in general and combinatorial optimization in particular, with a specific focus on metaheuristics, mathematical programming, intelligent search methods, and hybrid optimization approaches. His research work typically combines theory and practice for application areas such as scheduling, network design, transport optimization, logistics, and cutting and packing.

Günther Raidl is associate editor for the INFORMS Journal on Computingand the International Journal of Metaheuristics and at the editorial board of several journals including Algorithms, Metaheuristics and the Evolutionary Computation. He is co-founder and steering committee member of the annual European Conference on Evolutionary Computation in Combinatorial Optimization (EvoCOP). Since 2016 he is also faculty member of the Vienna Graduate School on Computational Optimization.

Günther Raidl has co-authored a text book on hybrid metaheuristics and over 180 reviewed articles in scientific journals, books, and conference proceedings. Moreover, he has co-edited 13 books and co-authored one book on hybrid metaheuristics. More information can be found at http://www.ac.tuwien.ac.at/raidl.