Sie haben Javascript deaktiviert!
Sie haben versucht eine Funktion zu nutzen, die nur mit Javascript möglich ist. Um sämtliche Funktionalitäten unserer Internetseite zu nutzen, aktivieren Sie bitte Javascript in Ihrem Browser.

Info-Icon This content is not available in English
Show image information
Thursday, 22.10.2020 | 16.15 - 17.15 Uhr | Zoom (see details)

Benefits and Perspectives of Automated Algorithm Selection for the Traveling Salesperson Problem


The Euclidean Traveling Salesperson Problem (TSP) is a classical optimization problem which is of high relevance for science and industry. Although it has been well-studied for decades, there is no algorithm in the class of inexact TSP optimization that is superior to all its competitors. Consequently, choosing the "right" algorithm for optimizing a given TSP instance is a challenging task in itself, and choosing the "wrong" algorithm can have severe implications on the overall performance. In recent years, automated algorithm selection has proven to be a very effective method to address this challenge in an automated way, thus improving the state of the art in this particular optimization domain. In my presentation, I will summarize the status quo of automated algorithm selection for the TSP and present some research perspectives in times of automation, digitization and big data.


Pascal Kerschke, Data Science: Statistics and Optimization, University of Münster


The lecture will be virtual and take place in Zoom:

Meeting ID: 972 1770 9819
One tap mobile
+13017158592,,97217709819# US (Germantown)
+13126266799,,97217709819# US (Chicago)

Dial by your location
        +1 301 715 8592 US (Germantown)
        +1 312 626 6799 US (Chicago)
        +1 346 248 7799 US (Houston)
        +1 408 638 0968 US (San Jose)
        +1 646 876 9923 US (New York)
        +1 669 900 6833 US (San Jose)
        +1 253 215 8782 US (Tacoma)
Meeting ID: 972 1770 9819
Find your local number:

Join by Skype for Business

The University for the Information Society